Systems and methods for generating an immune protocol for identifying and reversing immune disease

ABSTRACT

A system for generating an immune protocol for identifying and reversing immune disease is presented. The system comprising a computing device configured to receive at least an immune biomarker from a graphical user interface, determine a current immunological state of the user including an immune dysfunction as a function of the immune biomarker and an immune profile, assign an immune category to the immune profile as a function of the current immunological state, identify an effect on the immune profile for each nutritional element of a plurality of nutritional elements, determine at least a nutritional element that contributes to the immune category as a function of an immune machine-learning model and the nutritional input, identify a plurality of protocol elements, wherein each protocol element contains at least a nutrient amount intended to address the immunological dysfunction, and generate an immune protocol as a function of the plurality of protocol elements.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of Non-provisional application Ser.No. 17/136,215 filed on Dec. 29, 2020 and entitled “SYSTEMS AND METHODFOR GENERATING AN IMMUNE PROTOCOL FOR IDENTIFYING AND REVERSING IMMUNEDISEASE,” the entirety of which is incorporated herein by reference.

FIELD OF THE INVENTION

The present invention generally relates to the field of nutritionplanning for immunological dysfunction. In particular, the presentinvention is directed to systems and methods for generating an immuneprotocol for identifying and reversing immune disease.

BACKGROUND

Addressing immunological dysfunction is typically focused on receivinginput regarding allergic reactions, altering T cell proliferation, andtracking cellular damage. There exist difficulties in modelingimmunological disorders among the human population and generatingimmunological prophylaxis among the human population.

SUMMARY OF THE DISCLOSURE

In an aspect, a system for generating an immune protocol for identifyingand reversing immune disease includes a computing device configured toreceive at least an immune biomarker from a graphical user interface,determine a current immunological state of the user including an immunedysfunction as a function of the immune biomarker and an immune profile,assign an immune category to the immune profile as a function of thecurrent immunological state, identify an effect on the immune profilefor each nutritional element of a plurality of nutritional elementswherein the plurality of nutrition elements have been consumed by theuser and the identification includes receiving a nutritional input fromthe graphical user interface, retrieving a plurality of predictedeffects of the plurality of nutrient amounts on the immune profile as afunction of the nutritional input, and calculating an effect of thenutritional input on the immune profile, determine at least anutritional element that contributes to the immune category as afunction of an immune machine-learning model and the nutritional input,identify a plurality of protocol elements, wherein each protocol elementcontains at least a nutrient amount intended to address theimmunological dysfunction, and generate an immune protocol as a functionof the plurality of protocol elements.

In another aspect, a method for generating an immune protocol foridentifying and reversing immune disease includes receiving, at acomputing device, at least an immune biomarker from a graphical userinterface, determining, at a computing device, a current immunologicalstate of the user including an immune dysfunction as a function of theimmune biomarker and an immune profile, assigning, at a computingdevice, an immune category to the immune profile as a function of thecurrent immunological state, identifying, at a computing device, aneffect on the immune profile for each nutritional element of a pluralityof nutritional elements, wherein the plurality of nutrition elementshave been consumed by the user and the identification includes receivinga nutritional input from the graphical user interface, retrieving aplurality of predicted effects of the plurality of nutrient amounts onthe immune profile as a function of the nutritional input, andcalculating an effect of the nutritional input on the immune profile,determining, at a computing device, at least a nutritional element thatcontributes to the immune category as a function of an immunemachine-learning model and the nutritional input, identifying, at acomputing device, a plurality of protocol elements, wherein eachprotocol element contains at least a nutrient amount intended to addressthe immunological dysfunction, and generating, at a computing device, animmune protocol as a function of the plurality of protocol elements.

These and other aspects and features of non-limiting embodiments of thepresent invention will become apparent to those skilled in the art uponreview of the following description of specific non-limiting embodimentsof the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspectsof one or more embodiments of the invention. However, it should beunderstood that the present invention is not limited to the precisearrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is a block diagram illustrating a system for generating an immuneprotocol for identifying and reversing autoimmune disease;

FIG. 2 is a block diagram illustrating a machine-learning module;

FIG. 3 is a block diagram of an immune protocol database;

FIGS. 4A and 4B are a diagrammatic representation of an immune profile;

FIG. 5 is a diagrammatic representation of an immune protocol;

FIG. 6 is a diagrammatic representation of a user device;

FIG. 7 is a block diagram of a workflow of a method for generating animmune protocol for identifying and reversing autoimmune disease; and

FIG. 8 is a block diagram of a computing system that can be used toimplement any one or more of the methodologies disclosed herein and anyone or more portions thereof.

The drawings are not necessarily to scale and may be illustrated byphantom lines, diagrammatic representations and fragmentary views. Incertain instances, details that are not necessary for an understandingof the embodiments or that render other details difficult to perceivemay have been omitted.

DETAILED DESCRIPTION

At a high level, aspects of the present disclosure are directed tosystems and methods for generating an immune protocol for identifyingand reversing autoimmune disease. In an embodiment, computing device isconfigured to receive an immune biomarker and retrieve an immuneprofile. Computing device may generate immune profile by training amachine-learning model to derive relationships between immune biomarkersand the current immunological state of the user. Computing device isconfigured to assign the user to an autoimmunological category as afunction of the immune profile. From this autoimmune categorization,computing device may develop an immune protocol, which represents anautoimmune prophylaxis including identifying nutrition elementscontributing to the manifestation of autoimmune symptomology. Immuneprotocol may include determining an elimination plan to remove suchelements from the diet, and a reintroduction phase to increase thevariety of nutrition elements by identifying which elements may returnto the user's diet and the timing and manner with which thereintroduction may occur. Computing device may use machine-learning toderive nutrient amounts according to immunological effects on the user.In an embodiment computing device may generate an objective function togenerate combinations of protocol elements (e.g. ingredients, foods, andthe like) that achieve nutrient amounts without violating eliminationplan and/or reintroduction phase.

Referring now to FIG. 1, an exemplary embodiment of a system 100 for animmune protocol for identifying and reversing autoimmune disease isillustrated. System includes a computing device 104. Computing device104 may include any computing device as described in this disclosure,including without limitation a microcontroller, microprocessor, digitalsignal processor (DSP) and/or system on a chip (SoC) as described inthis disclosure. Computing device may include, be included in, and/orcommunicate with a mobile device such as a mobile telephone orsmartphone. Computing device 104 may include a single computing deviceoperating independently, or may include two or more computing deviceoperating in concert, in parallel, sequentially or the like; two or morecomputing devices may be included together in a single computing deviceor in two or more computing devices. Computing device 104 may interfaceor communicate with one or more additional devices as described below infurther detail via a network interface device. Network interface devicemay be utilized for connecting computing device 104 to one or more of avariety of networks, and one or more devices. Examples of a networkinterface device include, but are not limited to, a network interfacecard (e.g., a mobile network interface card, a LAN card), a modem, andany combination thereof. Examples of a network include, but are notlimited to, a wide area network (e.g., the Internet, an enterprisenetwork), a local area network (e.g., a network associated with anoffice, a building, a campus or other relatively small geographicspace), a telephone network, a data network associated with atelephone/voice provider (e.g., a mobile communications provider dataand/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network may employ a wiredand/or a wireless mode of communication. In general, any networktopology may be used. Information (e.g., data, software, and the like)may be communicated to and/or from a computer and/or a computing device.Computing device 104 may include but is not limited to, for example, acomputing device or cluster of computing devices in a first location anda second computing device or cluster of computing devices in a secondlocation. Computing device 104 may include one or more computing devicesdedicated to data storage, security, distribution of traffic for loadbalancing, and the like. Computing device 104 may distribute one or morecomputing tasks as described below across a plurality of computingdevices of computing device, which may operate in parallel, in series,redundantly, or in any other manner used for distribution of tasks ormemory between computing devices. Computing device 104 may beimplemented using a “shared nothing” architecture in which data iscached at the worker, in an embodiment, this may enable scalability ofsystem 100 and/or computing device.

With continued reference to FIG. 1, computing device 104 may be designedand/or configured to perform any method, method step, or sequence ofmethod steps in any embodiment described in this disclosure, in anyorder and with any degree of repetition. For instance, computing device104 may be configured to perform a single step or sequence repeatedlyuntil a desired or commanded outcome is achieved; repetition of a stepor a sequence of steps may be performed iteratively and/or recursivelyusing outputs of previous repetitions as inputs to subsequentrepetitions, aggregating inputs and/or outputs of repetitions to producean aggregate result, reduction or decrement of one or more variablessuch as global variables, and/or division of a larger processing taskinto a set of iteratively addressed smaller processing tasks. Computingdevice 104 may perform any step or sequence of steps as described inthis disclosure in parallel, such as simultaneously and/or substantiallysimultaneously performing a step two or more times using two or moreparallel threads, processor cores, or the like; division of tasksbetween parallel threads and/or processes may be performed according toany protocol suitable for division of tasks between iterations. Personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various ways in which steps, sequences of steps, processingtasks, and/or data may be subdivided, shared, or otherwise dealt withusing iteration, recursion, and/or parallel processing.

Continuing in reference to FIG. 1, computing device is configured toreceive at least an immune biomarker. An “immune biomarker,” as used inthis disclosure, is a biological and/or chemical substance or processthat is indicative of immunological functioning in the body. Immunebiomarker 108 may include biological molecules existing within alymphocyte subset (e.g., CD3+, CD4+, CD8+ T cells, B cells, NK Killercell,), plasma cell and antibody production, among other myeloid and/orlymphoid lineage cell, and/or a specific response (e.g. antibodyresponse) of the body to innate immunity, adaptive immunity, orauto-immunity. Immune biomarker may include measurements of complementfactors (e.g. C3 and C4), and other complement cascade biomolecules.Receiving the at least an immune biomarker 108 may include receiving aresult of one or more tests relating the user and/or analysis of one ormore tests. For instance and without limitation, an analysis of abiological extraction such as a blood panel test, lipid panel, genomicsequencing, and the like. Such data may be received and/or identifiedfrom a biological extraction of a user, which may include analysis of aphysical sample of a user such as blood, DNA, saliva, stool, and thelike, without limitation and as described in U.S. Nonprovisionalapplication Ser. No. 16/886,647, filed May 28, 2020, and entitled,“METHODS AND SYSTEMS FOR DETERMINING A PLURALITY OF BIOLOGICAL OUTCOMESUSING A PLURALITY OF DIMENSIONS OF BIOLOGICAL EXTRACTION USER DATA ANDARTIFICIAL INTELLIGENCE,” the entirety of which is incorporated hereinby reference.

Continuing in reference to FIG. 1, immune biomarker 108 may include testresults of screening and/or early detection of immunological disorders,diagnostic procedures, prognostic indicators from other diagnoses, frompredictors identified in a medical history, and information relating tobiomolecules associated with immunological function such as: CD3+, CD8+,CD11c+, CD1a+, IL-2, IL-2RA, JAK3, IL-15, TH1, CXCL10, CXCL9, Th2,IL-13, CCL17, CCL18, IL-12/Il-23p40, IL-32, IL-35, SCUBE-1, sCD40L,aminopeptidase N, vasorin precursor, alpha-1-antitrypsin, ceruloplasmin,and the like, and the like. A person skilled in the art having thebenefit of the entirety of this disclosure will be aware of variousadditional tests and/or immunological biomarkers that may be used bysystem 100.

Continuing in reference to FIG. 1, immune biomarker 108 may includeresults enumerating the identification of mutations in nucleic acidsequences. Immune biomarker 108 may include the presents of singlenucleotide polymorphisms (SNPs) in genetic sequences. Immune biomarker108 may include epigenetic factors, such as non-heritable alterations togenetic information. Immune biomarker 108 may include hematologicalanalysis including results from T-cell activation assays, radioimmunosorbent test (PRIST), enzyme linked immunosorbent assays (ELISA),radioimmunoassay (RIA), immunoradiometric assays (IRMA), luminescenceimmunoassays (LIA), and the like. Immune biomarker 108 may includereceiving medical history data, past diagnoses, current medications,Type-3 hypersensitivities, allergies, and user input such as a healthstate questionnaire, symptom complaint, and the like.

Continuing in reference to FIG. 1, computing device 104 may receiveimmune biomarker 108 as user input. User input may be received via a“graphical user interface,” which as used is this disclosure, is a formof a user interface that allows a user to interface with an electronicdevice through graphical icons, audio indicators, text-based interface,typed command labels, text navigation, and the like, wherein theinterface is configured to provide information to the user and acceptinput from the user. Graphical user interface may accept input, whereininput may include an interaction (e.g. replying to questionnaire,uploading a file, and the like) with a user device.

Continuing in reference to FIG. 1, immune biomarker 108 may be organizedinto immune training data sets. “Training data,” as used herein, is datacontaining correlations that a machine learning process, algorithm,and/or method may use to model relationships between two or morecategories of data elements. For instance, and without limitation,training data may include a plurality of data entries, each entryrepresenting a set of data elements that were recorded, received, and/orgenerated together; data elements may be correlated by shared existencein a given data entry, by proximity in a given data entry, or the like.Multiple data entries in training data may evince one or more trends incorrelations between categories of data elements; for instance, andwithout limitation, a higher value of a first data element belonging toa first category of data element may tend to correlate to a higher valueof a second data element belonging to a second category of data element,indicating a possible proportional or other mathematical relationshiplinking values belonging to the two categories. Multiple categories ofdata elements may be related in training data according to variouscorrelations; correlations may indicate causative and/or predictivelinks between categories of data elements, which may be modeled asrelationships such as mathematical relationships by machine learningprocesses as described in further detail below.

Continuing in reference to FIG. 1, immune biomarker 108 may be used togenerate training data for a machine-learning process. A “machinelearning process,” as used in this disclosure, is a process thatautomatedly uses a body of data known as “training data” and/or a“training set” to generate an algorithm (such as a collection of one ormore functions, equations, and the like) that will be performed by amachine-learning module to produce outputs given data provided asinputs; this is in contrast to a non-machine learning softwareprograming where the commands to be executed are determined in advanceby a subject and written in a programming language, as described infurther detail below.

Continuing in reference to FIG. 1, immune biomarker 108 may be organizedinto training data sets and stored and/or retrieved by computing device104, without limitation, as a relational database, a key-value retrievaldatabase such as a NOSQL database, or any other format or structure foruse as a database that a person skilled in the art may recognize assuitable upon review of the entirety of this disclosure. Immunebiomarker 108 training data may alternatively or additionally beimplemented using a distributed data storage protocol and/or datastructure, such as a distributed hash table and the like. Immunebiomarker 108 training data may include a plurality of data entriesand/or records, as described above. Data entries may be flagged with orlinked to one or more additional elements of information, which may bereflected in data entry cells and/or in linked tables such as tablesrelated by one or more indices in a relational database. Persons skilledin the art, upon reviewing the entirety of this disclosure, will beaware of various ways in which data entries of immune biomarkers may bestored, retrieved, organized, and/or reflect data and/or records as usedherein, as well as categories and/or populations of data consistent withthis disclosure.

Continuing in reference to FIG. 1, “immunological dysfunction,” as usedin this disclosure, is a disease and/or condition caused by adysfunction of the immune system. Immunological dysfunction may includeallergy, asthma, autoimmune diseases, autoinflammatory syndromes, andimmunological deficiency syndrome. Immunological deficiency may bebrought on by infection such as by measles, HIV, and the like.Immunological deficiency may be brought on by chronic and sustainedexposure to antigens such as by allergy, type-III hypersensitivity, andthe like. Immunological deficiency may be brought on by medical andpharmacological intervention and as a result of a secondary effect suchas with organ transplants, immunosuppressants, histamine intolerance,among many others. Immunological dysfunction may be brought on by anoveractive immunological response such as with eczema, asthma, hayfever, and the like. Immunological dysfunction may be caused by germlineand/or somatic genetic mutation such as in congenital disorders (SCID),aberrant gene expression, among many other non-pathogen events.

Continuing in reference to FIG. 1, computing device is configured toretrieve an immune profile related to the user. An “immune profile,” asused in this disclosure, is a determination about a currentimmunological state(s) of the user according to at least an immunebiomarker. A “current immunological state,” as used in this disclosure,is a metric that encapsulates the current state of autoimmune disorder,immunological priming, immune function, and the like, in the user. Acurrent immunological state may include a current propensity fordeveloping an immunological dysfunction. A current immunological statemay include “no dysfunction”. In individuals harboring no immunologicaldysfunction, a current state may include a likelihood of developing inthe future, or a percentile of immunological health according to asubset of alike users (e.g. classification). Immune profile 112 mayinclude any number of current immunological state determinationsincluding user's propensity for immunological dysfunction, current levelof immunological functioning, and/or their future likelihood fordysfunction. Immune profile 112 may include data represented bynumerical values, mathematical expressions, functions, matrices,vectors, and the like.

Continuing in reference to FIG. 1, immune profile 112 may includequalitative and/or quantitative summarization of the presence ofimmunological function, current and future risks of developing Type-3hypersensitivity, the level of current immunological function occupiedwith chronic disease (e.g. viral infections HIV-1, Varicella-zoster,Human Papilloma Virus (HPV), and the like), inflammation (e.g.rheumatoid arthritis), and the like. Immune profile 112 may includequalitative determinations, such as binary “yes”/“no” determinations forimmunological function dysfunction types, “normal”/“abnormal”determinations about the presence of and/or concentration of immunebiomarkers 108, for instance as compared to a normalized threshold valueof a biomarker among healthy adults. Immune profile 112 may includemathematical representations of the current state of immunologicalfunction, such as a function describing, for instance, the risk ofimmunological dysfunction as a function of time. Such representations ofimmune profile 112 may allow for determinations such as instantaneousimmunological risk, such as daily, weekly, monthly, and the like, risks.

Continuing in reference to FIG. 1, retrieving immune profile 112 mayinclude a process of searching for, locating, and returning immuneprofile 112 data. For example, immune profile 112 may be retrieved asdocumentation on a computer to be viewed or modified (e.g. files in adirectory, database, and the like). In non-limiting illustrativeembodiments, computing device 104 may locate and download immune profile112 via a web browser and the Internet.

Continuing in reference to FIG. 1, retrieving immune profile 112 mayinclude training an immune profile machine-learning model with trainingdata that includes a plurality of data entries wherein each entrycorrelates immune biomarkers 108 to a plurality of immune parameters andgenerating the immune profile 112 as a function of the immune profilemachine-learning model and the at least an immune biomarker 108. An“immune parameter,” as used in this discourse, is a parameter, metric,numerical value, or the like, that relates to quantifying immunebiomarker(s) 108. Immune parameter may include ranges of numericalvalues which represent ranges of cytokine levels (e.g. IL-2, IL-2RA,JAK3, IL-15, and the like), concentrations of biomarkers, and the likeImmune parameter may include threshold values, for instance biomarkersranges for a healthy adult, for comparing immune biomarker 108.

Continuing in reference to FIG. 1, immune profile machine-learning model116 may include any machine-learning algorithm (such as K-nearestneighbors algorithm, a lazy naïve Bayes algorithm, and the like),machine-learning process (such as supervised machine-learning,unsupervised machine-learning), or method (such as neural nets, deeplearning, and the like), as described in further detail below. Immuneprofile machine-learning model 116 may be trained to derive analgorithm, function, series of equations, or any mathematical operation,relationship, or heuristic, that can automatedly accept an input (immunebiomarker(s) 108) and assign a numerical value to, or otherwisecalculate an output (immune parameter(s)). Immune profilemachine-learning model 116 may derive individual functions describingunique relationships observed from the training data for each immunebiomarker 108, wherein different relationships may emerge between usersand user cohorts. Computing device 104 may generate the immune profile112 as a function of the immune profile machine-learning model 116 andthe at least an immune biomarker 108 (input). Immune profile 112 includeany number of immune parameters.

Continuing in reference to FIG. 1, training data for generating immuneprofile 112 may include results from biological extraction samples,health state questionnaires regarding symptomology, medical histories,physician assessments, lab work, and the like. Immune profile trainingdata may originate from the subject, for instance via a questionnaireand a user interface with computing device 104 to provide medicalhistory data. Receiving immune profile training data may includereceiving whole genome sequencing, gene expression patterns, and thelike, for instance as provided by a genomic sequencing entity, hospital,database, the Internet, and the like Immune profile training data mayinclude raw data values recorded and transmitted to computing device 104via a wearable device such as a pedometer, gyrometer, accelerometer,motion tracking device, bioimpedance device, ECG/EKG/EEG monitor,physiological sensors, blood pressure monitor, blood sugar and volatileorganic compound (VOC) monitor, and the like. Immune profile trainingdata may originate from an individual other than user, including forinstance a physician, lab technician, nurse, caretaker, psychologist,therapist, and the like. Immune profile training data may be input intocomputing device 104 via a graphical user interface for instance for ahealth state questionnaire for onboarding of user symptomology. It isimportant to note that training data for machine-learning processes,algorithms, and/or models used herein may originate from any sourcedescribed for immune profile training data.

Continuing in reference to FIG. 1, in non-limiting illustrativeexamples, the expression levels of a variety of cytokines as it relatesto immunological priming, as identified above, may be retrieved from adatabase, such as a repository of peer-reviewed research (e.g. NationalCenter for Biotechnology Information is part of the United StatesNational Library of Medicine), and the trained immune profilemachine-learning model 116 derived function(s) may calculate an averageand statistical evaluation (mean±S.D.) from the data, across which theuser's cytokine levels are compared. In such an example, immune profilemachine-learning model 116 may derive a scoring function that includes arelationship for how to arrive at an immune parameter numerical valueaccording to the user's level of gene expression (e.g. number of mRNAtranscripts per tissue) as it relates to the average and statisticalevaluation in normal tissue expression.

Continuing in reference to FIG. 1, immune profile 112 may becomeincreasingly more complete, and more robust, with increasing numbers ofimmune parameters, describing larger sets of immune biomarkers 108 inthe user. Immune parameter may be generated for each biomarker gene (orset of genes) described above; each white blood cell type (or set ofwhite blood cell type); among other factors. Immune profilemachine-learning model 116 may derive a unique algorithm for developingindividual immune parameters from the plurality of immune biomarkers108. Immune profile machine-learning model 116 may derive functions,systems of equations, matrices, and the like, that describe and/orincorporate relationships between sets of immune biomarkers 108(training data), for instance combining the expression level of two ormore genes, multiplied by scalar coefficients according to the presenceof SNPs (single nucleotide polymorphisms) or mutations present in thegenes, dividing by the ratio of phosphorylated-unphosphorylated states,ubiquitinated states, and the like In the full spectrum of cellsignaling, maintaining cellular homeostasis, cell division, proteindegradation, among other biological phenomenon that may contribute tothe development of immunological dysfunction; immune profilemachine-learning model 116 may derive increasingly complicatedalgorithms for combining immune biomarkers 108 into immune parameterssummarized in immune profile 112.

Continuing in reference to FIG. 1, computing device 104 is configured toassign the immune profile 112 to an immune category, wherein the immunecategory is a determination about a current immunological state of theuser according to the at least an immune biomarker 108. An “immunecategory,” as used in this disclosure, is a designation of animmunological dysfunction. Immune category 120 may include “noimmunological dysfunction”. In the instance that a user has no apparentimmunological dysfunction, user may be assigned immune category 120 withwhich the user most closely resembles. Immune category 120 may includetissue or organ type classification, such as “celiac disease”, “Gravesdisease”, “Hashimoto thyroiditis”, “type I diabetes mellitus”, “Addisondisease”, and the like Immune category 120 may include a designationregarding a type of immunological dysfunction that may not involve aparticular tissue such as “type III hypersensitivity”, “immune primingissue”, “allergic reaction type”, “autoimmune disorder”, and the likeImmune category 120 may include a predictive immunologicalclassification, where a user does not currently have a particulardysfunction but may include data that indicates an immune category 120with which they may be most closely categorized to in the future. Forinstance, a family history of arthritis and a combination of epigeneticelements (as summarized in immune profile 112) may classify anindividual in “arthritis” immune category 120, despite not currentlyhaving arthritis. Immune profile 112 may have associated with it anidentifier, such as a label, that corresponds to an immune category 120.

Continuing in reference to FIG. 1, assigning immune profile 112 toimmune category 120 may include classifying the immune profile 112 to animmune category 120 using an immune classification machine-learningprocess. Classification using immune classification machine-learningprocess 124 may include identifying which set of categories (immunecategory 120) an observation (immune profile 112) belongs.Classification may include clustering based on pattern recognition,wherein the presence of immune biomarkers 108, such as geneticindicators, symptoms, and the like, identified in immune profile 112relate to a particular immune category 120. Such classification methodsmay include binary classification, where the immune profile 112 issimply matched to each existing immune category 120 and sorted into acategory based on a “yes”/“no” match. Classification may includeweighting, scoring, or otherwise assigning a numerical value to dataelements in immune profile 112 as it relates to each immune category120. Such a score may represent a “likelihood”, probability, or othernumerical data that relates to the classification into immune category120, where the highest score is selected depending on the definition of“highest”.

Continuing in reference to FIG. 1, immune classificationmachine-learning process 124 may include any machine-learning process,method, and/or algorithm, as described in further detail below. Immuneclassification machine-learning process 124 may generate a “classifier”using training data. A classifier may include a machine-learning model,such as a mathematical model, neural net, or program generated by amachine learning algorithm known as a “classification algorithm,” asdescribed in further detail below. Such a classifier may sort inputs(such as the data in the immune profile 112) into categories or bins ofdata (such as classifying the data into an immune category 120),outputting the bins of data and/or labels associated therewith. Trainingdata used for such a classifier may include a set of immune profile 112training data (as described above) as it relates to classes ofimmunological dysfunction, organ/tissue, symptoms, severity, and thelike for instance, training data may include ranges of biomarker valuesas they relate to the variety of dysfunctions. Classificationmachine-learning algorithm may be performed by machine-learning module,as described in further detail below. Classification may be performedusing, without limitation, linear classifiers such as without limitationlogistic regression and/or naive Bayes classifiers, nearest neighborclassifiers such as k-nearest neighbors classifiers, support vectormachines, least squares support vector machines, fisher's lineardiscriminant, quadratic classifiers, decision trees, boosted trees,random forest classifiers, learning vector quantization, and/or neuralnetwork-based classifiers. As a non-limiting example, an immune profile112 training data classifier may classify elements of training data toelements that characterizes a sub-population, such as a subset of immunebiomarker 108 (e.g. gene expression patterns as it relates to a varietyof immune categories 120) and/or other analyzed items and/or phenomenafor which a subset of training data may be selected.

Continuing in reference to FIG. 1, computing device may classify theimmune profile 112 to an immune category 120 using an immuneclassification machine-learning process 124 and assign the immunecategory 124 as a function of the classification. For instance andwithout limitation, training data may include sets of immune parametersand/or immune biomarkers 108, as described above. Immune classificationmachine-learning process 124 may be trained with training data to“learn” how to categorize a user's immune profile 112 to immunecategories 120 as a function of trends in mutations, gene expression,SNPs, user symptomology, and the like. Training data may originate fromuser input, for instance via a health state questionnaire via agraphical user interface, may originate from a biological extractiontest result such as genetic sequencing, blood panel, lipid panel.Training data may originate from a user's medical history, a wearabledevice, a family history of disease. Training data may similarlyoriginate from any source, as described above, for immune biomarker 108and determining immune profile 112.

Continuing in reference to FIG. 1, computing device 104 is configured todetermine, using the immune category 120 and the immune profile 112, anelimination plan. An “elimination plan,” as used in this disclosure, isan identity of at least a nutrition element to be removed from a user'scurrent consumption to alleviate a condition associated with immuneprofile and/or immune category. A “nutrition element,” as used in thisdisclosure, is an item that includes a nutrient intended to be usedand/or consumed by user. A “nutrient,” as used in this disclosure,” is abiologically active compound whose consumption is intended for thetreatment of symptoms of immunological dysfunction and/or prevention ofimmunological dysfunction.

Continuing in reference to FIG. 1, elimination plan 128 may includepatterns in currently consumed foods, medications, stimulants,supplements, and the like that may contribute to immunologicaldysfunction, autoimmunity, and/or symptomology matching immunebiomarkers 108. For instance, alcohol, tobacco, oils, gluten, foodadditives, non-steroidal anti-inflammatory drugs (NSAIDs), and the like,determined to contribute to gut inflammation, Hashimoto's thyroiditis,rheumatoid arthritis, celiac disease, lupus, and the like For example,nutrition elements that may be identified in an elimination plan 128 mayinclude grains, legumes, nuts, seeds, nightshade vegetables (e.g.eggplant, tomato, peppers, and the like), eggs, dairy, and the likeElimination plan 128 may include nutrition elements associated withallergy.

Continuing in reference to FIG. 1, determining elimination plan 128includes identifying an effect on the immune profile for each nutritionelement of a plurality of nutrition elements, wherein the plurality ofnutrition elements have been consumed by the user. An “effect on theimmune profile,” as used in this disclosure, is a change, consequence,and/or result in at least an immune biomarker 108, immune category 120,and/or likelihood of immunological dysfunction in a user due toconsumption of an amount of a nutrient. An effect of a nutrient may be“no effect”. Calculating an effect of a nutrient may include determininghow an immune biomarker 108 may change, such as an increase/decreaseaccording to a particular amount of the nutrient. For instance andwithout limitation, such a calculation may include determining theeffect of chronic, sustained nutrient amounts in a diet for weeks,months, and the like

Continuing in reference to FIG. 1, identifying an effect on the immuneprofile for each nutrition element of a plurality of nutrition elementsmay include receiving nutritional input from the user. A “nutritionalinput,” as used in this disclosure, is an amount of a nutrient consumedby a user. Computing device 104 may receive nutritional input from userto identify at least more accurately a nutrition element for eliminationplan 128. Nutritional input 132, for instance and without limitation,may include food items that have associated nutrition facts, whereincomputing device 104 may calculate, weight, or otherwise modify, thenutritional input 132 data (e.g. using a weighting factor). This mayresult in accurate, per-user nutritional input 132 corrected formetabolic differences, nutrient pharmacokinetics, current immunologicalstates, and the like. Receiving nutritional input, for instance andwithout limitation, may be performed as described in Ser. No.16/911,994, filed Jun. 25, 2020, titled “METHODS AND SYSTEMS FORADDITIVE MANUFACTURING OF NUTRITIONAL SUPPLEMENT SERVINGS,” the entiretyof which is incorporated herein by reference. System 100 may receivenutritional input from a user, retrieve from an application (e.g.calorie tracking application), database, download from the Internet,from physician, and the like

Continuing in reference to FIG. 1, determining the effect of theplurality of nutrient amounts on the immune profile 112 may includeretrieving a plurality of predicted effects of the plurality of nutrientamounts on the immune profile 112 as a function of the at least animmune biomarker 108. A “predicted effect” of a nutrient or combinationof nutrients as used in this disclosure, is a hypothesis about theoutcome for a user after consuming a nutrient amount and/or amount of acombination of nutrients. Predicted effect, herein, may refer simply toany effect calculated, determined, and/or output by computing device 104according to receiving an input associated with the user. Retrieving aplurality of predicted effects may include retrieving from a database, aresearch repository, or wherever computing device 104 may recognize arelationship between a nutrient amount and immunological dysfunction.Retrieving a plurality of predicted effects may include, for instance,searching using the immune profile 112 (and immune category 120), a webbrowser and the Internet, for the plurality of predicted effects. Insome embodiments, retrieving a plurality of predicted effects mayinclude determining at least a predicted effect, for instance byderiving a function from a machine-learning algorithm. A predictedeffect of a plurality of nutrient amounts may include the effect onimmune category 120, immune biomarker 108, immune parameter, likelihoodof immunological dysfunction, and the like, due to a particular nutrientamount, or combination of nutrient amounts.

Continuing in reference to FIG. 1, identifying an effect on the immuneprofile 112 may include calculating an effect of nutritional input 132on immune profile 112. Calculating an effect may include a mathematicaloperation, such as subtraction, addition, and the like Calculating aneffect of a nutrient may include retrieving an empirical formula,equation, and/or function that describes relationships between anutrient and immune biomarker 108, test result, immune parameter, andthe like Calculating an effect of a nutrient may include deriving analgorithm, function, or the like, for instance using a machine-learningprocess and/or model. Calculating such an effect using machine-learningmay include training data that includes a plurality of nutrients as itrelates to effects on immune categories 120, immune biomarkers 108, andthe like

Continuing in reference to FIG. 1, identifying an effect on the immuneprofile 112 for each nutrition element of a plurality of nutritionelements from nutritional input 132 may include machine-learning.Computing device 104 may train a machine-learning process with trainingdata that includes a plurality of data entries wherein each data entrycorrelates nutritional input 132 to effects on immunologicaldysfunction. Training data may include a variety of meals, foods, menuitems, medications, and the like, with corresponding labels that mayidentify the items, for instance from parsing the data in thenutritional input 132 to identify individual nutrition elements.Training data may include nutrient amounts for the constituent nutritionelements in each nutritional input 132. Training data may includepharmacokinetics data relating to how “well” a user absorbs a nutrientamount from nutrition element. Training data may include data setsdescribing effects of nutrient amounts on immune biomarkers 108.Training data may include immunological dysfunctions according to immunebiomarkers 108. Training data may originate from any source as describedabove, such as from a database, web browser and the Internet, the user,wearable device, a physician, medical history, biological extractiontest result, and the like In this way, a machine-learning process (e.g.a neural net) may be trained by computing device 104 to identify allpredicted effects (output) from nutritional input 132 (input) accordingto the relationships derived between each, and the hidden layers (nodes)from nutritional input 132 to constituent ingredients to nutrientamounts to effects on immune biomarkers 108 to immunological dysfunctionaccording to an individualized immune profile 112 and immune category120. Such a machine-learning process may include any machine-learningalgorithm, as performed by a machine-learning module described infurther detail below. Computing device 104 using such a trainedmachine-learning process for determining an effect may accept an inputof the immune profile 112 and nutritional input 132 and determine howwhat a user is consuming is affecting the immune profile 112.

Continuing in reference to FIG. 1, in non-limiting illustrativeexamples, elimination plan 128 may include determining if a change inimmune category 120 may arise from adding and/or removing a nutrientfrom nutritional input 132, for instance changing an immune category 120from “high risk type III hypersensitivity” to “moderate risk” andpossibly “low risk” with increasing or decreasing dietary short chainfatty acids (C3-C7), medium chain fatty acids (C8-C10), C1-C8 alkylalcohols, very long-chain fatty alcohols (VLCFA; C24-34), vitamin E,vitamin K, and the like, while eliminating animal milks, peanuts, eggs,soy products, wheat, gluten, shellfish, sesame seed, tree nuts (e.g.pistachio, cashew, walnut, almond, hazelnut, macadamia), among othernutrition elements associated with type III hypersensitivity. Type IIIhypersensitivity reactions may include an abnormal immune responsemediated by the formation of antigen-antibody aggregates called “immunecomplexes.” Immune complexes may precipitate in various tissues such asskin, joints, blood vessels, and/or glomeruli, and trigger the classicalcomplement pathway. Losing control of the formation of immune complexes,or chronically high or inadequately cleared by innate immune cells, theimmune complex formation accumulated in the body may lead to seriousimmunological dysfunction, including symptomatology associated withsystemic lupus erythematosus, rheumatoid arthritis, serum sickness,Farmer's lung, and the like. Controlling the timing and manner ofeliminating (and potential reintroduction of) nutrient elementsidentified in nutritional input 132 may help identify which specificnutrient elements are troublesome for a user and be included inelimination plan 128.

Continuing in reference to FIG. 1, identifying an effect of nutritionelements, medications, stimulants, supplements, and the like, fordetermining elimination plan 128 may include calculating an “immuneindex” of a nutrition element. An “immune index,” as used in thisdisclosure, is a score that informs the magnitude of effect nutritionalinput 132 may have on the function of the immune system in the user.Such an immune index may include training a machine-learning model withtraining data including a plurality of data entries which may correlatenutrition amounts to effects on immune profile 112. Such a trainedmachine-learning model may derive a scoring function for assigning ascore and indexing nutrition elements based on their nutrient contentand the score they may be assigned according to their immunologicalimpact. Such an immune index may be used to judge, compare, and/oridentify, by placing a logical scoring index on each item to be directlycompared. An elimination plan may include an “elimination score”, whichas used in this disclosure, is a score that may inform which itemsshould be removed from the nutritional input 132. An elimination scoremay be generated like immune index. An elimination score may include athreshold value (e.g. numerical value), above which nutrition elementsmay be eliminated from nutritional input 132.

Continuing in reference to FIG. 1, determining elimination plan 128includes determining, of the identified effect, at least a nutritionelement that may contribute to the immune category 120. Elimination plan128 may be determined based on which nutrition elements may be removedfrom nutritional input to place user into a different immune category120. Placing user into a different immune category 120 may includeplacing the user into a less severe immunological dysfunction, categoryindicating ameliorating symptomology, and the like

Continuing in reference to FIG. 1, determining at least a nutritionelement that may contribute to the immune category 120 may includereceiving immune training data. “Immune training data,” as used in thisdisclosure, is training data that includes a plurality of data entrieswherein each entry correlates a plurality of nutrition amounts to immunebiomarkers 108. Immune training data may include any training data setcontaining data entries, as described above. Immune training data mayoriginate from any source as described above, such as from a database,web browser and the Internet, the user, wearable device, a physician,medical history, biological extraction test result, and the like Immunetraining data may include data entries such as nutrient amounts,thresholds, values, as described for subsets of users based on age, sex,diagnosis, disease, fitness level, and the like Immune training data mayinclude data entries such as how immune biomarkers 108 (e.g. interleukincytokines, TNF isoforms, T cell activation, macrophage/granulocyteactivity, and the like) are affected by chronic and acute nutrientdeficiency, food dyes, commercially-available medications, and the like

Continuing in reference to FIG. 1, determining at least a nutritionelement that may contribute to the immune category 120 may includetraining an immune machine-learning model with immune training data.Immune machine-learning model 136 may include any machine-learningprocess, algorithm, and/or model as performed by machine-learning moduledescribed in further detail below.

Continuing in reference to FIG. 1, determining at least a nutritionelement that may contribute to the immune category may includedetermining at least a nutrition element that may contribute to theimmune category 120 as a function of the immune machine-learning model136 and the nutritional input 132 from the user. Immune machine-learningmodel 136 may be trained with immune training data, as described above,to derive functions, equations, and/or mathematical relationships thatmay exist between nutrient amounts and immune category 120 based onrelationships observed in the training data. Such relationships, forinstance, may include how the immune profile 112 (immune parameters andimmune biomarkers 108) is affected by carrying amounts of nutrients.Deriving such a function may include calculating a series of numericalvalues, where for each input (nutrient amount) an output (effect onimmune category) may be automatedly determined based on therelationships observed in training data.

Continuing in reference to FIG. 1, in non-limiting illustrativeexamples, immune machine-learning model 136 may derive a relationshipbetween nutritional input 132 and immune category 120 based effectsobserved between nutrition elements and biomarkers. For instance, PPARgamma agonists may inhibit T-cell proliferation by blocking theproduction of IL-2, potentially alleviating a variety of symptomsassociated with immunological dysfunction, such as polycystic ovarysyndrome (PCOS). PCOS patients have low expression of PPART in skeletalmuscle that it was associated with insulin resistance (IR). Treatmentswith PPAR agonists ameliorated muscle IR and increased the expression ofPPART with overall increase in mitochondrial biogenesis and function.Moreover, enhancing triglyceride accumulation in adipose tissue, PPARTagonists are able to diminish lipotoxicity-associated immunologicaldysfunction. This relationship may be correlated to nutrition elements(e.g. protocol elements, as described in further detail below) anddietary paradigms (immune protocol, as described in further detailbelow) that may enhance activity and/or concentration of PPARs (immunebiomarker 108). For instance, low-calorie diets to achieve weight lossor maintaining a healthy weight, limiting intake of simple sugars,refined carbohydrates, and intake foods with lower glycemic index,reduction of saturated and trans fatty acids and attention to possibledeficiencies such as vitamin D, chromium, and omega-3. In such anexample, immune machine-learning model 136 may determine the effect ofnutritional input 132 on PPAR expression and determine at what thresholdlevel of PPAR the immune category 120 shifts. Achieving a shift mayinclude eliminating some nutritional elements from nutritional input132. Those nutritional elements may be identified in elimination plan128.

Continuing in reference to FIG. 1, computing device 104 is configured tocreate, using the elimination plan 128, a reintroduction phase. A“reintroduction phase,” as used in this disclosure, is an identity of anutrition element whose consumption is to be initially avoided, alongwith a timeline (frequency) and dosage (serving size) for reintroducingthe nutrition element back into the user's diet to allow the widestdietary variety a person can tolerate. Reintroduction phase 140 mayinclude a plurality of identities of nutrition elements and/or nutrientamounts that a user is to be “inoculated” against. Such inoculation mayinclude first removing the nutrition element from the diet andcontrolling the manner and timing with which to reintroduce thenutrition element to the diet. Such reintroduction phase 140 mayrepresent a method to systematically eliminate extrinsic sources ofimmunological dysfunction and more accurately identify autoimmunity(immunological dysfunction due to intrinsic factors). Reintroductionphase 140 may assist in identifying food intolerances, allergies,antigens that may contribute to type III hypersensitivities, and thelike. Reintroduction phase 140 may assist in identifying immunologicaldysfunction previously unknown to user.

Continuing in reference to FIG. 1, creating the reintroduction phase 140includes identifying a frequency associated with the at least anutrition element determined in the elimination plan 128. A “frequency,”as used in this disclosure, is a number of consumption occurrencesassociated with a time course, such as daily, weekly, monthly, and thelike, of which a nutrition element is intended to be consumed. Frequencymay be determined as a function of the identified effect in eliminationplan 128, wherein the frequency of consumption is tailored to provide asufficient minimal nutrient level over a time. Identifying the frequencyassociated with the at least a nutrition element determined inelimination plan 128 may include calculating a consumption time of theat least a nutrition elements as a function of the identified effect inelimination plan 128. A consumption time may include a time of day,which days per week, and the like Frequency may include dosages, forinstance and without limitation, a particular number of doses of NSAIDs,chromium, vitamin C, and the like, in a 24 hour period, how many mealsper week may be permitted to include an item expected to cause anallergy such as shellfish, dairy, and the like Identifying a frequencymay include retrieving data, for instance via a web browser and theInternet, database, and the like, which includes instructions for whento supplement consumption with elimination plan 128 nutrition elements.Identifying a frequency may include retrieving an empirical equation,formula, function, and the like, to calculate the frequency as an outputfrom an input of elimination plan 128. Calculating a frequency mayinclude any mathematical operation (e.g. subtraction, addition, and thelike), for instance adding a nutrient amount to nutritional input 132per frequency to arrive at a calculated total frequency.

Continuing in reference to FIG. 1, identifying a frequency inreintroduction plan 140 may include training a machine-learning model todetermine the frequency. Machine-learning model may be with trainingdata including data entries correlating nutrient amounts to effects onimmune categories 120, such as immune training data. Training data mayinclude data sets including data entries that correlate microdosing avariety of nutrient amounts to immune biomarkers 108. Training amachine-learning model with such training data may result in deriving afunction that may be used to calculate a threshold value of nutrientamounts to reintroduction phase 140 frequencies (e.g. once every 5meals, twice per week maximum, and the like). Training data mayoriginate from any source as described above, such as from a database,web browser and the Internet, the user, wearable device, a physician,medical history, biological extraction test result, and the like

Continuing in reference to FIG. 1, creating the reintroduction phase 140includes identifying a magnitude associated with the at least anutrition element determined in elimination plan 128. A “magnitude,” asused in this disclosure, is a serving size of at least a nutritionelement as a function of the identified effect in the elimination plan128. Identifying the magnitude associated with the at least a nutritionelement may include calculating a serving size of the at least anutrition element as a function of the identified effect in theelimination plan 128. A nutrition element magnitude may include acalculated nutrient amount. Nutrient amounts may include dosages, forinstance and without limitation, a particular dosage of NSAIDs (mg/kg),gluten (g/day), and the like Identifying a magnitude may includeretrieving data, for instance via a web browser and the Internet,database, and the like, which includes instructions for how muchnutrient amount from the elimination plan 128 to add to user's diet.Identifying a magnitude may include retrieving an empirical equation,formula, function, and the like, to calculate the frequency (output)from elimination plan 128 (input). Calculating a magnitude may includeany mathematical operation (e.g. subtraction, addition, and the like),for instance adding a nutrient amount to nutritional input 132 atvarying frequencies to arrive at a calculated total magnitude ofnutrient amount.

Continuing in reference to FIG. 1, identifying a magnitude may includetraining a machine-learning model with training data to derive afunction describing relationships observed in the training data. Suchtraining data may include effects the nutrient amounts have on immunecategory, such as immune training data. Training data may include dataentries that correlate microdosing a variety of nutrient amounts toimmune biomarkers 108. Training a machine-learning model with suchtraining data may result in a function that may derive a threshold valueof nutrient amounts to threshold values of immune biomarkers 108 (i.e.maximum value of x mg/kg nutrient amount #1 for staying below maximumvalue of y mg/L immune biomarker #1). Training data may originate fromany source as described above, such as from a database, web browser andthe Internet, the user, wearable device, a physician, medical history,biological extraction test result, and the like Training data set andmachine-learning model used for deriving frequency may be the same forderiving magnitude; however, the functions, equations, formulas, and thelike, derived from the training data may solve for different variables,use different coefficients, and the like, in deterring both frequencyand magnitude. For instance a multi-variable equation, wherein asmagnitude changes, frequency may change at a different rate.

Continuing in reference to FIG. 1, determining reintroduction phase 140(or elimination plan 128) may include using a classificationmachine-learning process. As described above, a classificationmachine-learning process may use training data to generate a classifier(machine-learning model) which may sort input data into categories andoutput the bins of data. Such a classification machine-learning processmay be trained with training data that relates ingredient serving sizesto effect of autoimmune symptoms, especially in cohorts of alike users.Where cohorts of users that share immune category 120 may be used toderive elimination plan 128. For instance, identifying common nutritionelements between 1,000+ users that share identical immunologicaldysfunction symptoms. Training data for such a classifier may includenutrition elements identified in elimination plan 128 and/or frequenciesand magnitudes identified in reintroduction phase 140 from subsets ofalike users. A classifier trained with this training data may “learn”how to sort nutritional input 132 and immune category 120 to eliminationplan 128 and reintroduction phase 140. In non-limiting illustrationembodiments, such classification may be used as a “starting point”, uponwhich a machine-learning model may derive more accurate elimination plan128 nutrition elements, and more accurate reintroduction phase 140frequencies and magnitudes, wherein the reintroduction phase 140 is forrestoring the widest variety of diet possible for a user whileaddressing immunological dysfunction. Training data for such aclassifier may originate from any source as described above, such asfrom a database, web browser and the Internet, the user, wearabledevice, a physician, medical history, biological extraction test result,and the like

Continuing in reference to FIG. 1, reintroduction phase 140 may includecalculating a reintroduction score. A “reintroduction score,” as used inthis disclosure, is a numerical value, score, metric, or the like, thatinforms if (and when) a user should reintroduce the item(s) to theirdiet. Such a score may be informed based on user preference, forinstance, if the user wants to maintain caffeinated products,reintroduction could start there (e.g. higher score elements).Reintroduction phase 140 may notify user when to begin, and what rangeof nutrition element should be consumed according to the reintroductionscore. Reintroduction score may be generated as a scoring function froma machine-learning process, algorithm, and/or model, as describedherein, performed by machine-learning module described in further detailbelow. Training data may include a plurality of data entries correlatinguser preference for nutrition element to its elimination score andimmune index, as defined above. Training data machine-learning processwith such training data may derive a scoring function that penalizesnutrition elements with heavy deleterious immunological effect, whileplacing scores that prioritize other nutrient elements. Training datafor such a machine-learning process may originate from any source asdescribed herein, such as from a database, web browser and the Internet,the user, wearable device, a physician, medical history, biologicalextraction test result, and the like

Continuing in reference to FIG. 1, computing device 104 is configured toidentify a plurality of protocol elements, wherein each protocol elementcontains at least a nutrient amount intended to address immunologicaldysfunction. A “protocol element,” as used in this disclosure, is anutrient amount specifically curated to address immunologicaldysfunction, symptomology, and/or prevent immunological dysfunction inthe future. “Curating” protocol elements 144, as used in thisdisclosure, is a process of combining ingredients and/or nutrientamounts according to what is beneficial for each user. Curated protocolelements 144 may include combining ingredients such as spices,plant-based materials, animal products, probiotic cultures, vitaminsupplements, trace amounts, and the like, to result in a custom protocolelement 144, such as a particular “health shake”, unique dish, or thelike, which may not be commercially available. Protocol element 144 mayinclude alimentary elements, such as meals (e.g. chicken parmesan withGreek salad and iced tea), food items (e.g. French fries), grocery items(e.g. broccoli), health supplements (e.g. whey protein), beverages (e.g.orange juice), and the like. Protocol element 144 may be “personalized”in that nutrition elements are curated in a guided manner according toimmune profile 112, gene expression patterns, immune biomarkers 108,immune category 120, treatment type (T-Car therapy, hormone treatment,surgery, taxanes, cisplatin, and the like), and the like. Protocolelement 144 may include supplementary use of oral digestive enzymes andprobiotics which may also have merit as immunological modulatorymeasures.

Continuing in reference to FIG. 1, protocol element 144 may includespecific micronutrient, macronutrient, and the like, profile. Forinstance and without limitation, protocol element 144 may includespecific predetermined amounts of propionic acid, chromium, and thelike, which may result in a significant decrease in TGFβ1, IL-10, andFoxp3. In some instances, worsening in MS (multiple sclerosis;autoimmune disease) animal models was observed with a diet comprisinglong-chain fatty acids, whereas mice prophylactically given propionicacid showed significant improvement in the progression of disease; theremay be similar results in psoriasis (another autoimmune disease).Protocol elements 144 may include a specific dietary category, such as a“ketogenic diet”, “low glycemic index diet”, “Paleo diet”, and so on.

Continuing in reference in FIG. 1, protocol element 144 may includenutrient amounts intended to address immunological dysfunction accordingto predicted effects on immune biomarker 108. In non-limitingillustrative examples, experimental autoimmune encephalomyelitis (EAE)induced by sensitization with myelin oligodendrocyte glycoprotein (MOG)is a T cell-dependent autoimmune disease that reproduces theinflammatory demyelinating pathology of multiple sclerosis. Theencephalitogenic T cell response to MOG may be either induced oralternatively suppressed as a consequence of immunologicalcross-reactivity, or “molecular mimicry” with the extracellular IgV-likedomain of the milk protein butyrophilin (BTN). In animal models,providing native BTN triggers an inflammatory response in the centralnervous system (CNS) characterized by the formation of scatteredmeningeal and perivascular infiltrates of T cells and macrophages. Itmay be demonstrated that this pathology is mediated by an MHC classII-restricted T cell response that cross-reacts with the MOG peptidesequence. Conversely, molecular mimicry with BTN may be similarlyexploited to suppress immunological dysfunction in MOG-induced users. Itmay be demonstrated that immunological dysfunction may be ameliorated bytreatment with the homologous BTN peptide found in animal milk protocolelements 144, but that the protective effect may also be observed inactively induced disease following transmucosal (intranasal)administration of the peptide. These results identify a mechanism bywhich the consumption of milk products may modulate the pathogenicautoimmune response to MOG. In such an instance, individuals with immuneprofile 112 indicated MOG-induced immunological dysfunction may beprovided animal milk products as protocol elements 144, regardless of ifthe element was observed in nutritional input 132.

Continuing in reference to FIG. 1, continuing in further non-limitingillustrative examples, computing device 104 may derive the aboverelationship between milk proteins and immunological dysfunction tocurate protocol element 144. For instance, whey and casein proteinsupplement products, which include protein profiles derived from animalmilk (cow's milk) may lack other antigens such as lactose (lactoseintolerance), certain fatty acids, among others. An elimination plan 128may identify milk products to be eliminated due to their effect onimmune biomarkers 108. A reintroduction phase may accommodate thereintroduction of small servings of whey and/or casein, progressingfirst back to a limited pool of lactose-free animal product, and finallyto animal product.

Continuing in reference to FIG. 1, identifying a plurality of protocolelements 144 may include training a nutrition machine-learning processwith training data, wherein training data includes a plurality of dataentries that correlates a plurality of nutrient effects to a pluralityof nutrient amounts for each immune category 120. Nutritionmachine-learning process 148 may include any machine-learning process,algorithm, and/or model described herein, as performed by amachine-learning module described in further detail below. Nutritionmachine-learning process 148 may train with training data that includesprotocol elements 144 identified for each immune category 120. Nutritionmachine-learning process 148 may derive relationships in nutrientamounts that relate to particular immune category 120. For instance andwithout limitation, foods identified to be associated with immunologicaldysfunction may reveal patterns in nutrients that have yet to beidentified by endocrinologists, immunologists, dieticians, and the like.Trained nutrition machine-learning process 148 may generate a function(or series of functions) which describe alterations to nutritionelements (e.g. elimination plan 128 and/or reintroduction phase 140)calculated directly from immune profile 112, prior to classification toimmune category 120. Training data for nutrition machine-learningprocess 148 may include a plurality of data entries that correlatesnutrient amounts (and their associated effects, as described herein) toimmune category 120. Such training data may include vitamin and mineralamounts to particular immunological dysfunction. A machine-learningmodel trained with such data may “learn” to output protocol elements 144as a function of input (immune category 120). Nutrition machine-learningprocess training data for such a machine-learning process may originatefrom any source as described herein, such as from a database, webbrowser and the Internet, the user, wearable device, a physician,medical history, biological extraction test result, and the like

Continuing in reference to FIG. 1, identifying a plurality of protocolelements 144 may include calculating the at least a nutrient amount as afunction of the immune category 120 of the user. A “nutrient amount,” asused in this disclosure, is a numerical value(s) relating to the amountof a nutrient. A “nutrient amount,” as used in this disclosure, is anumerical value(s) relating to the amount of a nutrient. Nutrient amount152 may include mass amounts of a vitamin, mineral, macronutrient(carbohydrate, protein, fat), a numerical value of calories, massamounts of phytonutrients, antioxidants, bioactive ingredients,nutraceuticals, and the like. Calculating the at least a nutrient amount152, may include using trained nutrition machine-learning process 148 toautomatedly calculate nutrient amounts 152 (e.g. mg/kg, mg/cal, mg/gmacromolecule, and the like) as a function of the immune category 120(input). Calculating nutrient amounts 152 in this manner may includederiving functions, equations, and the like, from relationships observedin the training data, as described above.

Continuing in reference to FIG. 1, identifying a plurality of protocolelements 144 may include identifying the plurality of protocol elements144 according to the nutrition machine-learning process 148 and the atleast a nutrient amount 152. Identifying protocol elements 144 mayinclude using the trained nutrition machine-learning process 148 togenerate outputs (nutrient amounts 152) and use a mathematical operation(e.g. subtraction) to locate protocol elements 144 (nutrition elements),subtracting the amount of the nutrient from the target nutrient amount152, to select appropriate protocol elements 144, determine servingsizes, and the like Alternatively or additionally, identifying protocolelements 144 may include training nutrition machine-learning process 148to generate a classifier, or to additionally add a decision nodes (e.g.neural net layer) to automatedly output protocol elements 144 from thefirst set of outputs (nutrient amounts 152). For instance, nutritionmachine-learning process 148 may be trained with training data thatincludes a plurality of data entries correlating nutrient amounts 152 toprotocol elements 144. In this way, as soon as nutrient amounts 152 aredetermined, protocol elements 144 that supply the appropriate amountsmay be retrieved. “Appropriate” may include using, for instance andwithout limitation, at threshold value of nutrient amount 152 output bynutrition machine-learning process 144, against which protocol elements144 may be selected and compared. Such a comparison may yield protocolelements 144 that are selected if they are above or below the thresholdvalue.

Continuing in reference to FIG. 1, computing device 104 may calculatenutrient amounts 152, for instance, by using a default amount, such asfrom a standard 2,000 calorie diet, and increasing and/or decreasing theamount according to a numerical scale associated with immune parametersin the immune profile 112. Such a calculation may include a mathematicaloperation such as subtraction, addition, multiplication, and the like;alternatively or additionally, such a calculation may involve deriving aloss function, vector analysis, linear algebra, system of questions, andthe like, depending on the granularity of the process. Deriving such aprocess for the calculating may include nutrition machine-learningprocess 148. Nutrient amounts 152 may include threshold values, orranges or values, for instance and without limitation, between 100-500mcg chromium (+3) per 24 hours, wherein the range changes as a functionof immune profile 112. Nutrient amounts 152 may be calculated as heatmaps (or similar mathematical arrangements), for instance using banding,where each datum of immune profile 112 elicits a particular range of aparticular nutrient amount 152 or set of amounts. In non-limitingillustrative examples, such a calculation may include querying for andretrieving a standard amount of water soluble vitamins for a healthyadult, for instance as described below in Table 1:

TABLE 1 Nutrient Amount Vitamin C 60 mg/day Thiamin (B1) 0.5 mg/1,000kcal; 1.0 mg/day Riboflavin (B2) 0.6 mg/1,000 kcal; 1.2 mg/day Niacin(B3) 6.6 NE/1,000 kcal; 13 ND/day Vitamin B6 0.02 mg/1 g protein; 2.2mg/day Vitamin B12 3 μg/day Folic Acid 400 μg/day

Continuing in reference to FIG. 1, in reference to Table 1 above,wherein NE is niacin equivalent (1 mg niacin, or 60 mg tryptophan), mg(milligram), kcal (1000 kcal=1 Calorie), and μg (microgram). Computingdevice 104 may store and/or retrieve the above standard nutrientamounts, for instance in a database. The amounts may be re-calculatedand converted according to a user's immune profile 112. For instance,these amounts may relate to an average BMI, older male, classified torheumatoid arthritis immune category 102, for any range of calories, butmay be adjusted according to unique user-specific immune biomarkers 108.For example, an obese woman who has been placed on a strict 1,600Calorie/day diet, curated according to identified risk factors (immunebiomarkers 108) may need the above amounts recalculated according to thecalorie constraint (threshold), where some vitamin amounts may increase,some may decrease, and some may remain constant. For instance, if such aperson were to suffer from lupus, a particular increase among vitamin Cmay be calculated according to a weighting factor associated with lupus;with MOG-based and/or PPAR-based immunological dysfunction, vitamin Cmay increase by a different amount, but vitamin A from retinol sources(animal products) may need to decrease, and so on among many otherimmunological dysfunction.

Continuing in reference to FIG. 1, computing device 104 may identify theplurality of protocol elements 144 by using nutrient amount 152 as aninput and generating combinations, lists, or other aggregates ofprotocol elements 144 necessary to achieve nutrient amount 152. Forinstance, computing device 104 may use a template nutrient amount 152 of‘200 mg vitamin C’ and build a catalogue of protocol elements 144 untilthe 200 mg vitamin C value is obtained. Computing device 104 may performthis task by querying for food items, for instance from a menu, grocerylist, or the like, retrieving the vitamin C content, and subtracting thevalue from the nutrient amount 152. In non-limiting illustrativeexamples, computing device 104 may identify orange juice (90 mg vitaminC/serving; 200 mg−90 mg=110 mg) for breakfast, Brussel sprouts (50 mgvitamin C/serving; 110 mg−50 mg=60 mg) for lunch, and baked potato (20mg vitamin C/serving) and spicy lentil curry (40 mg vitamin C/serving;60 mg−(20 mg+40 mg)=0 mg) for dinner. In such an example, computingdevice 104 may search according to a set of instructions (e.g. foodpreferences, allergies, restrictions, and the like) present in an immuneprofile 112, provided by a physician, user, or the like, and subtracteach identified Protocol element 144 nutrient amount from nutrientamount 152 until a combination of protocol elements 144 that representsa solution is found. Once a solution is found, computing device 104 maygenerate a file of protocol elements 144 and store in a database, asdescribed in further detail below.

Continuing in reference to FIG. 1, calculating nutrient amounts 152 mayinclude deriving a weighting factor to adjust, or otherwisere-calculate, nutrient amount 152. Weighting factor may be determined bycomputing device 104, for instance, by querying for vitamin amountsaccording to data inputs identified in the immune profile 112. Forinstance in non-limiting illustrative examples, evidence for aninhibitory effect of vitamin D3 on the progression of autoimmunearthritis may come from animal models of arthritis, namely murine Lymearthritis and collagen-induced arthritis. Development of human Lymearthritis in mice produced arthritic lesions including footpad and ankleswelling (immune biomarker 108). Supplementation with1,25-dihydroxycholecalciferol (calcitriol; vitamin D vitamer; naturallyoccurring form) of an adequate diet fed to mice with arthritis mayminimized or prevented these symptoms. Mice immunized with type IIcollagen also developed arthritis, leading to an autoimmune response tointrinsic type II collagen (immune biomarker 108). The symptoms of thisdisease may also be prevented by dietary supplementation with1,25-dihydroxycholecalciferol (1,25-(OH)2D3; vitamin D). 1,25-(OH)2D3may have an effect on the inhibition of effector T-cell responses by inthe induction of Treg populations. For example, in bulk cultures ofhuman CD4+CD25− T cells and putative naïve T cells, 1,25-(OH)2D3increased in the presence of IL-2 and the frequency ofactivation-induced FoxP3+ T cells expressing high levels of theinhibitory receptor CTLA-4 (cytotoxic T lymphocyte antigen 4). In suchan example, users exhibiting an immune biomarker 108 of these effectors,cytokines, and/or symptomology, may require a particular nutrient amount152 with regard to vitamin D and the relationship 1,25-(OH)2D3 may haveon other vitamins. Specific amounts of vitamin D supplementation mayprovide a significant reduction in the pro-inflammatory cytokines IFN-7and IL-17. The capacity of 1,25-(OH)2D3 to promote tolerogenic T cellfunctions in humans may be further supported by the observation thatvitamin D supplementation and pharmacologic treatment with biologicallyactive vitamin D increased the level of serum- or T cell-associatedTGF-β and IL-10, representing improvement of current immunologicalstate. Modulation of particular autoimmune symptomology may be done withvitamin D supplementation. But, increasing the amount of vitamin D forall individuals as a blanket suggestion may exacerbate some immunecategories. This necessitates careful consideration of immunebiomarkers, disease states, and current nutrient levels.

Continuing in reference to FIG. 1, in non-limiting illustrativeexamples, nutrition machine-learning process 148 may employ amachine-learning algorithm to derive per-user pharmacokinetics of avitamin, such as vitamin B6. The machine-learning algorithm may acceptan input of values including the total amount of protein consumed (ingrams) and total amount of vitamin B6 consumed (in mg) per day in adiet, and what the serum levels of the vitamin B6 vitamer,pyridoxal-5-phosphate, over the course of a month, and derive the ratesof metabolism, or how ‘well’ the user is obtaining the vitamin fromprotocol elements 144 and adsorbing vitamin B6. In other words, thealgorithm may derive a function (e.g. using linear regression, vectorquantization, least squares, and the like) that describes thepharmacokinetics for that particular user regarding what amount ofvitamin B6 consumed, per amount of dietary protein, results in whatcorresponding amount of bioactive vitamin compound, as measured by theblood vitamer from a biological extraction. Such a function, obtainedfrom machine-learning, may then be used by computing device 104 with aninput of the immune profile 112, to calculate an output which is a moreaccurate, customized, per-user nutrient amount 152 of vitamin B6.Persons skilled in the art, upon the benefit of reviewing thisdisclosure in its entirety, may appreciate that this process may berepeated and completed for the full spectrum of nutrients, both requiredas part of a diet and not required as part of a diet, to generate highlyaccurate and specific protocol elements 144.

Continuing in reference to FIG. 1, additionally, in non-limitingillustrative examples, computing device 104 may relate theconcentrations of the metabolic products related to vitamins (e.g.vitamers), minerals, phytonutrients, antioxidative compounds,nutraceuticals, prodrugs, and the like, to their effectiveconcentrations in tissues related to various immune categories 120. Forinstance, computing device 104 may additionally search and retrieve datathat relates the blood levels of the vitamin B6 vitamer,pyridoxal-5-phosphate, to the effective concentrations of vitamin B6 injoints, which is particularly sensitive to vitamin B6-sensitive ofmarkers of inflammation in rheumatoid arthritis. Computing device 104may store the values in a “look-up table”, or graph a relationship as amathematical function, among other ways of representing a data structurethat relates the data identified in the search. Alternatively oradditionally, computing device 104 may derive a function, for instanceusing nutrition machine-learning process, which relates theconcentration of the compound in a particular biological extraction,such as blood, to varying amounts in tissues such as joints, pancreas,kidneys, and the like This may prove helpful in calculating nutrientamounts 152 as a function of user consumption to specific targetnutrient amount 152 quantities within a particular organ/tissueaccording to the input data in the immune profile 112. Persons skilledin the art, after review of this disclosure in its entirety, mayappreciate that each immune category 120, of 100+ different patterns ofimmunological dysfunction, may have a unique algorithm for identifyingnutrient amounts 152, of the 100's of distinct nutrients identified. Forinstance and without limitation, each allergy type, tissue/organaffected, stage of immunological dysfunction, type III hypersensitivity,per-user pharmacokinetics, nutrition deficiency, immune biomarker 108,immune profile 112, and the like, may elicit a different mathematicalequation for calculating each individual nutrient amount 152.

Continuing in reference to FIG. 1, generate an immune protocol as afunction of the elimination plan, the reintroduction phase, and theplurality of protocol elements. An “immune protocol,” as used in thisdisclosure, is a collection of nutrient amounts 152 and protocolelements 144 organized according to constraints in the elimination plan128 and reintroduction phase 140. Immune protocol 156 may include afrequency (timing) and dosage (serving size) schedule for protocolelements 144. Immune protocol 156 may include gathering, classifying, orotherwise categorizing nutrient amounts 152, protocol elements 144lists, or the like, which incorporates immunologically-specific dietaryrecommendations. For instance, protocol elements 144 may be scored witha numerical score scale that associates a meal, snack, beverage,supplement, and the like, with preventing immunological dysfunction,benefit to immunological dysfunction, and the like. Immune protocol 156may include selecting protocol elements 144 according to a thresholdscore, where items above are selected and arranged. Threshold score mayinclude a daily threshold, wherein protocol elements 144 are selectedeach day according to the threshold; and threshold may include anumerical value relating to nutrient amount 152, among other outputs ofsystem 100 described herein. Determining immune protocol 156 may includemachine-learning. For instance, training a machine-learning model toidentify a scoring rubric for building the immune protocol 156 based onsome criteria such as autoimmune prevention, lowering biomarker levels,clearing type III hypersensitivity, among other criteria. Immuneprotocol 156 may relate specific immune categories 120 to specificnutrients of interest and provide protocol element 144 scheduling timesand serving sizes for each meal. Immune protocol 156 may differ from oneuser to the next according to the magnitude of the disease outline(immune category 120 and immune profile 112).

Continuing in reference to FIG. 1, immune protocol 156 may include arecommended nutrition plan and a recommended supplement plan that atleast addresses immune biomarker 108, mitigate symptoms, side-effects,and the like Immune protocol 156 may contain a plan with timing ofmeals, calorie amounts, vitamin amounts, mineral amounts, and the likeImmune protocol 156 may include food items combined with a supplement ofnon-food items. Immune protocol 156 may be presented as a function ofpreventing immunological dysfunction for non-dysfunction users, forinstance an otherwise healthy person to reduce their lifelong risk ofdeveloping allergies, intolerances, and the like

Continuing in reference to FIG. 1, generating the immune protocol mayinclude generating an objective function with the plurality of protocolelements 144, wherein the objection function outputs at least anordering of the plurality of protocol elements 144 according toconstraints in the reintroduction phase 140. An “objective function,” asused in this disclosure, is a mathematical function that may be used bycomputing device 104 to score each possible combination of protocolelements 144, wherein the objective function may refer to anymathematical optimization (mathematical programming) to select the‘best’ element from a set of available alternatives. Selecting the‘best’ element from a set of available alternatives may include acombination of protocol elements 144 which achieves the nutrient amounts152 in addressing immune profile 112 in a user.

Continuing in reference to FIG. 1, an objective function 160 may includeperforming a greedy algorithm process. A “greedy algorithm” is definedas an algorithm that selects locally optimal choices, which may or maynot generate a globally optimal solution. For instance, computing device104 may select combinations of protocol elements 144 so that valuesassociated therewith are the best value for each category. For instance,in non-limiting illustrative example, optimization may determine thecombination of the most efficacious ‘serving size’, ‘timing ofconsumption’, ‘probiotic product’, ‘vegetable’, and the like, categoriesto provide a combination that may include several locally optimalsolutions but may or may not be globally optimal in combination.

Still referring to FIG. 1, objective function 160 may be formulated as alinear objective function, which computing device 104 may solve using alinear program, such as without limitation, a mixed-integer program. A“linear program,” as used in this disclosure, is a program thatoptimizes a linear objective function, given at least a constraint; alinear program may be referred to without limitation as a “linearoptimization” process and/or algorithm. For instance, in non-limitingillustrative examples, a given constraint might be a metabolic disorderof a user (e.g. lactose intolerance, poor absorption, food allergy, userpreference, and the like), and a linear program may use a linearobjective function to calculate combinations, considering how theselimitations effect combinations. In various embodiments, system 100 maydetermine a set of instructions towards addressing a subject's immuneprofile 112 that maximizes a total prevention score subject to aconstraint that there are other competing objectives. For instance, ifachieving one nutrient amount 152 by selecting from each protocolelement 144 may result in needing to select a second protocol element144, wherein each may compete in prevention (e.g. adopting two or morediet types simultaneously may not be feasible, and the like). Amathematical solver may be implemented to solve for the set ofinstructions that maximizes scores; mathematical solver may beimplemented on computing device 104 and/or another device in system 100,and/or may be implemented on third-party solver.

With continued reference to FIG. 1, objective function may includeminimizing a loss function, where a “loss function” is an expression anoutput of which a process minimizes to generate an optimal result. Forinstance, achieving nutrient amounts 152 may be set to a nominal value,such as ‘100’, wherein the objective function selects elements incombination that reduce the value to ‘0’, wherein the nutrient amounts152 are ‘100% achieved’. In such an example, ‘maximizing’ would beselecting the combination of protocol elements 144 that results inachieving nutrient amounts 152 by minimizing the difference. As anon-limiting example, computing device 104 may assign variables relatingto a set of parameters, which may correspond to autoimmune preventioncomponents, calculate an output of mathematical expression using thevariables, and select an objective that produces an output having thelowest size, according to a given definition of “size.” Selection ofdifferent loss functions may result in identification of differentpotential combinations as generating minimal outputs, and thus‘maximizing’ efficacy of the combination.

Continuing in reference to FIG. 1, generating the immune protocol 144may include generating an immune score. An “immune score,” as used inthis disclosure, is a numerical value, metric, parameter, and the like,described by a function, vector, matrix, or any other mathematicalarrangement, which enumerates a user's current nourishment as it relatesto immunological dysfunction alleviation, wherein the immune scorereflects the level of user participation in the immune protocol 144.Immune score 164 may include the lifelong risk of developingimmunological dysfunction. Such a score may increase with participationin immune protocol 156 and/or decrease by falling short of nutrientamounts 152, depending on criteria selected for a score increase and/ordecrease. Immune score 164 may include using a machine-learning process,algorithm, and/or model to derive a numerical scale along which toprovide a numerical value according to a user's immune profile 112 andparticipation in immune protocol 156 generated from immune profile 112.For instance, such a machine-learning model may be trained with trainingdata, wherein training data contains data entries of nutrient amounts152 correlated to preventing immunological dysfunction. Such amachine-learning model with said training data may be used by computingdevice 104 to relate nutritional input 132 to achieving some level ofnutrient amount 152, and how the nutrient amount 152 relates toalleviation and prevention of dysfunction, removing allergens, reducingimmune complex formation, and the like

Continuing in reference to FIG. 1, in non-limiting illustratingexamples, falling short of very long-chain fatty acids and vitamin Dnutrient amounts 152, may have a particular effect on immune score 164for an individual who has been classified to “IL-15 driven immunologicaldisorder” immune category 120. Where, chronically falling short of thenutrient amount 152 results in a (−5 score) each month but fallingwithin the nutrient amount 152 range for those two nutrients affords (+2score for each) every month; the target amount for the preceding monthmay dictate the score change for each subsequent month. In such a case,a machine-learning model may derive an algorithm which dictates theamount to increase/decrease immune score 164 for that particular immunecategory 120 according to the nutrient amounts 152. Such amachine-learning model may be trained to identify the relationshipbetween nutrient amounts 152 and effect on immunological dysfunction toderive an equation that relates scoring criteria. Alternatively oradditionally, computing device 104 may “learn” how to provide a scoreand change said score based on retrieving the effect of nutrient amounts152 (e.g. immune machine-learning model 136 outputs). Immune score 164may then be output using the model and nutritional input 132, as inputs.Nutritional input 132 may be used to determine immune protocol 164, aswell as, be used to calculate immune score 164.

Continuing in reference to FIG. 1, generating immune protocol 156 mayinclude receiving a user preference regarding the plurality of protocolelements 144, and modifying the immune protocol 156 as a function of theuser preference. A “user preference,” as used in this disclosure, is auser input that designates a preference related to at least a protocolelement 144. User preference 152 may include designations of protocolelements 144 to avoid and/or include such as particular food groups,condiments, spices, dietary restrictions (e.g. no animal products),cuisine type (e.g. Mediterranean foods), time of day for eating (e.g.fasting before 10 am). In this way, computing device 104 may accept aninput of user preference 152 filter, sort, classify, or otherwise modifythe data structure of protocol elements 144 (and associated data) andarrange the protocol elements 144 into immune protocol 156 in a custom,per-user manner. Computing device 104 may modify the plurality ofprotocol elements 144 as a function of the user preference 144, forinstance by providing recipes with steps omitted, new steps added, orentirely new recipes altogether utilizing the same or different protocolelements 144. Computing device 104 may modify the plurality of protocolelements 144 as a function of the user preference 144 by generating anew file, based on the preference, and storing and/or retrieving thefile from a database, as described in further detail below.

Referring now to FIG. 2, an exemplary embodiment of a machine-learningmodule 200 that may perform one or more machine-learning processes asdescribed in this disclosure is illustrated. Machine-learning module mayperform determinations, classification, and/or analysis steps, methods,processes, or the like as described in this disclosure using machinelearning processes. A “machine learning process,” as used in thisdisclosure, is a process that automatedly uses training data 204 togenerate an algorithm that will be performed by a computingdevice/module to produce outputs 208 given data provided as inputs 212;this is in contrast to a non-machine learning software program where thecommands to be executed are determined in advance by a subject andwritten in a programming language.

Still referring to FIG. 2, “training data,” as used herein, is datacontaining correlations that a machine-learning process may use to modelrelationships between two or more categories of data elements. Forinstance, and without limitation, training data 204 may include aplurality of data entries, each entry representing a set of dataelements that were recorded, received, and/or generated together; dataelements may be correlated by shared existence in a given data entry, byproximity in a given data entry, or the like. Multiple data entries intraining data 204 may evince one or more trends in correlations betweencategories of data elements; for instance, and without limitation, ahigher value of a first data element belonging to a first category ofdata element may tend to correlate to a higher value of a second dataelement belonging to a second category of data element, indicating apossible proportional or other mathematical relationship linking valuesbelonging to the two categories. Multiple categories of data elementsmay be related in training data 204 according to various correlations;correlations may indicate causative and/or predictive links betweencategories of data elements, which may be modeled as relationships suchas mathematical relationships by machine-learning processes as describedin further detail below. Training data 204 may be formatted and/ororganized by categories of data elements, for instance by associatingdata elements with one or more descriptors corresponding to categoriesof data elements. As a non-limiting example, training data 204 mayinclude data entered in standardized forms by persons or processes, suchthat entry of a given data element in a given field in a form may bemapped to one or more descriptors of categories. Elements in trainingdata 204 may be linked to descriptors of categories by tags, tokens, orother data elements; for instance, and without limitation, training data204 may be provided in fixed-length formats, formats linking positionsof data to categories such as comma-separated value (CSV) formats and/orself-describing formats such as extensible markup language (XMIL),JavaScript Object Notation (JSON), or the like, enabling processes ordevices to detect categories of data.

Alternatively or additionally, and continuing to refer to FIG. 2,training data 204 may include one or more elements that are notcategorized; that is, training data 204 may not be formatted or containdescriptors for some elements of data. Machine-learning algorithmsand/or other processes may sort training data 204 according to one ormore categorizations using, for instance, natural language processingalgorithms, tokenization, detection of correlated values in raw data andthe like; categories may be generated using correlation and/or otherprocessing algorithms. As a non-limiting example, in a corpus of text,phrases making up a number “n” of compound words, such as nouns modifiedby other nouns, may be identified according to a statisticallysignificant prevalence of n-grams containing such words in a particularorder; such an n-gram may be categorized as an element of language suchas a “word” to be tracked similarly to single words, generating a newcategory as a result of statistical analysis. Similarly, in a data entryincluding some textual data, a person's name may be identified byreference to a list, dictionary, or other compendium of terms,permitting ad-hoc categorization by machine-learning algorithms, and/orautomated association of data in the data entry with descriptors or intoa given format. The ability to categorize data entries automatedly mayenable the same training data 204 to be made applicable for two or moredistinct machine-learning algorithms as described in further detailherein. Training data 204 used by machine-learning module 200 maycorrelate any input data as described in this disclosure to any outputdata as described in this disclosure.

Further referring to FIG. 2, training data may be filtered, sorted,and/or selected using one or more supervised and/or unsupervisedmachine-learning processes and/or models as described in further detailherein; such models may include without limitation a training dataclassifier 216. Training data classifier 216 may include a “classifier,”which as used in this disclosure is a machine-learning model as definedherein, such as a mathematical model, neural net, or program generatedby a machine learning algorithm known as a “classification algorithm,”as described in further detail herein, that sorts inputs into categoriesor bins of data, outputting the categories or bins of data and/or labelsassociated therewith. A classifier may be configured to output at leasta datum that labels or otherwise identifies a set of data that areclustered together, found to be close under a distance metric asdescribed below, or the like. Machine-learning module 200 may generate aclassifier using a classification algorithm, defined as a processwhereby a computing device and/or any module and/or component operatingthereon derives a classifier from training data 204. Classification maybe performed using, without limitation, linear classifiers such aswithout limitation logistic regression and/or naive Bayes classifiers,nearest neighbor classifiers such as k-nearest neighbors classifiers,support vector machines, least squares support vector machines, fisher'slinear discriminant, quadratic classifiers, decision trees, boostedtrees, random forest classifiers, learning vector quantization, and/orneural network-based classifiers. As a non-limiting example, trainingdata classifier 216 may classify elements of training data to elementsthat characterizes a sub-population, such as a subset of immunebiomarkers 108 (such as patterns in cytokine levels, gene expression,and the like, as it relates to immune profile 112) and/or other analyzeditems and/or phenomena for which a subset of training data may beselected.

Still referring to FIG. 2, machine-learning module 200 may be configuredto perform a lazy-learning process 220 and/or protocol, which mayalternatively be referred to as a “lazy loading” or “call-when-needed”process and/or protocol, may be a process whereby machine learning isconducted upon receipt of an input to be converted to an output, bycombining the input and training set to derive the algorithm to be usedto produce the output on demand. For instance, an initial set ofpredictions may be performed to cover an initial heuristic and/or “firstguess” at an output and/or relationship. As a non-limiting example, aninitial heuristic may include a ranking of associations between inputsand elements of training data 204. Heuristic may include selecting somenumber of highest-ranking associations and/or training data 204elements, such as classifying immune biomarker 108 elements to immuneprofile 112 elements and assigning a value as a function of some rankingassociation between elements. Lazy learning may implement any suitablelazy learning algorithm, including without limitation a K-nearestneighbors algorithm, a lazy naïve Bayes algorithm, or the like; personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various lazy-learning algorithms that may be applied togenerate outputs as described in this disclosure, including withoutlimitation lazy learning applications of machine-learning algorithms asdescribed in further detail herein.

Alternatively or additionally, and with continued reference to FIG. 2,machine-learning processes as described in this disclosure may be usedto generate machine-learning models 224. A “machine-learning model,” asused in this disclosure, is a mathematical and/or algorithmicrepresentation of a relationship between inputs and outputs, asgenerated using any machine-learning process including withoutlimitation any process as described above and stored in memory; an inputis submitted to a machine-learning model 224 once created, whichgenerates an output based on the relationship that was derived. Forinstance, and without limitation, a linear regression model, generatedusing a linear regression algorithm, may compute a linear combination ofinput data using coefficients derived during machine-learning processesto calculate an output datum. As a further non-limiting example, amachine-learning model 224 may be generated by creating an artificialneural network, such as a convolutional neural network comprising aninput layer of nodes, one or more intermediate layers, and an outputlayer of nodes. Connections between nodes may be created via the processof “training” the network, in which elements from a training data 204set are applied to the input nodes, a suitable training algorithm (suchas Levenberg-Marquardt, conjugate gradient, simulated annealing, orother algorithms) is then used to adjust the connections and weightsbetween nodes in adjacent layers of the neural network to produce thedesired values at the output nodes. This process is sometimes referredto as deep learning. A machine-learning model may be used to derivenumerical scales for providing numerical values to immune profile 112,immune parameters, and/or immune score 164, and the like, as describedabove, to “learn” the upper and lower limits to the numerical scale, theincrements to providing scoring, and the criteria for increasing anddecreasing elements encompassed in the immune profile 112 and/or immunescore 164, and the like A machine-learning model may be used to “learn”which elements of immune biomarkers 108 have what effect on immuneprofile 112, and which elements of immune profile 112 are affected byparticular protocol elements 144 and the magnitude of effect, and thelike The magnitude of the effect may be enumerated and provided as partof system 100, where protocol elements 144 are communicated to user fortheir immunological properties.

Still referring to FIG. 2, machine-learning algorithms may include atleast a supervised machine-learning process 228. At least a supervisedmachine-learning process 228, as defined herein, include algorithms thatreceive a training set relating a number of inputs to a number ofoutputs, and seek to find one or more mathematical relations relatinginputs to outputs, where each of the one or more mathematical relationsis optimal according to some criterion specified to the algorithm usingsome scoring function. For instance, a supervised learning algorithm mayinclude an immune profile 112 (potentially classified into immunecategories 120), as described above as inputs, nutrient element 120outputs, and a ranking function representing a desired form ofrelationship to be detected between inputs and outputs; ranking functionmay, for instance, seek to maximize the probability that a given input(such as nutrient amounts 152) and/or combination of inputs isassociated with a given output (immune protocol 156 that incorporateprotocol elements 144 to achieve nutrient amounts 152 that are ‘best’for immune category 120) to minimize the probability that a given inputis not associated with a given output, for instance finding the mostsuitable times to consume meals, and what the meals should be, and thelike Ranking function may be expressed as a risk function representingan “expected loss” of an algorithm relating inputs to outputs, whereloss is computed as an error function representing a degree to which aprediction generated by the relation is incorrect when compared to agiven input-output pair provided in training data 204. Persons skilledin the art, upon reviewing the entirety of this disclosure, will beaware of various possible variations of at least a supervisedmachine-learning process 228 that may be used to determine relationbetween inputs and outputs. Supervised machine-learning processes mayinclude classification algorithms as defined above.

Further referring to FIG. 2, machine learning processes may include atleast an unsupervised machine-learning process 232. An unsupervisedmachine-learning process 232, as used herein, is a process that derivesinferences in datasets without regard to labels; as a result, anunsupervised machine-learning process 232 may be free to discover anystructure, relationship, and/or correlation provided in the data.Unsupervised processes may not require a response variable; unsupervisedprocesses may be used to find interesting patterns and/or inferencesbetween variables, to determine a degree of correlation between two ormore variables, or the like.

Still referring to FIG. 2, machine-learning module 200 may be designedand configured to create a machine-learning model 224 using techniquesfor development of linear regression models. Linear regression modelsmay include ordinary least squares regression, which aims to minimizethe square of the difference between predicted outcomes and actualoutcomes according to an appropriate norm for measuring such adifference (e.g. a vector-space distance norm); coefficients of theresulting linear equation may be modified to improve minimization.Linear regression models may include ridge regression methods, where thefunction to be minimized includes the least-squares function plus termmultiplying the square of each coefficient by a scalar amount topenalize large coefficients. Linear regression models may include leastabsolute shrinkage and selection operator (LASSO) models, in which ridgeregression is combined with multiplying the least-squares term by afactor of 1 divided by double the number of samples. Linear regressionmodels may include a multi-task lasso model wherein the norm applied inthe least-squares term of the lasso model is the Frobenius normamounting to the square root of the sum of squares of all terms. Linearregression models may include the elastic net model, a multi-taskelastic net model, a least angle regression model, a LARS lasso model,an orthogonal matching pursuit model, a Bayesian regression model, alogistic regression model, a stochastic gradient descent model, aperceptron model, a passive aggressive algorithm, a robustnessregression model, a Huber regression model, or any other suitable modelthat may occur to persons skilled in the art upon reviewing the entiretyof this disclosure. Linear regression models may be generalized in anembodiment to polynomial regression models, whereby a polynomialequation (e.g. a quadratic, cubic or higher-order equation) providing abest predicted output/actual output fit is sought; similar methods tothose described above may be applied to minimize error functions, aswill be apparent to persons skilled in the art upon reviewing theentirety of this disclosure.

Continuing to refer to FIG. 2, machine-learning algorithms may include,without limitation, linear discriminant analysis. Machine-learningalgorithm may include quadratic discriminate analysis. Machine-learningalgorithms may include kernel ridge regression. Machine-learningalgorithms may include support vector machines, including withoutlimitation support vector classification-based regression processes.Machine-learning algorithms may include stochastic gradient descentalgorithms, including classification and regression algorithms based onstochastic gradient descent. Machine-learning algorithms may includenearest neighbors algorithms. Machine-learning algorithms may includeGaussian processes such as Gaussian Process Regression. Machine-learningalgorithms may include cross-decomposition algorithms, including partialleast squares and/or canonical correlation analysis. Machine-learningalgorithms may include naïve Bayes methods. Machine-learning algorithmsmay include algorithms based on decision trees, such as decision treeclassification or regression algorithms. Machine-learning algorithms mayinclude ensemble methods such as bagging meta-estimator, forest ofrandomized tress, AdaBoost, gradient tree boosting, and/or votingclassifier methods. Machine-learning algorithms may include neural netalgorithms, including convolutional neural net processes.

Still referring to FIG. 2, models may be generated using alternative oradditional artificial intelligence methods, including without limitationby creating an artificial neural network, such as a convolutional neuralnetwork comprising an input layer of nodes, one or more intermediatelayers, and an output layer of nodes. Connections between nodes may becreated via the process of “training” the network, in which elementsfrom a training data 204 set are applied to the input nodes, a suitabletraining algorithm (such as Levenberg-Marquardt, conjugate gradient,simulated annealing, or other algorithms) is then used to adjust theconnections and weights between nodes in adjacent layers of the neuralnetwork to produce the desired values at the output nodes. This processis sometimes referred to as deep learning. This network may be trainedusing training data 204.

Referring now to FIG. 3, a non-limiting exemplary embodiment 300 of animmune protocol database 304 is illustrated. Immune biomarker 108 for aplurality of subjects, for instance for generating a training dataclassifier 216, may be stored and/or retrieved in immune protocoldatabase 304. Immune biomarker 108 data from a plurality of subjects forgenerating training data 204 may also be stored and/or retrieved fromimmune protocol database 304. Computing device 104 may receive, store,and/or retrieve training data 204, wearable device data, physiologicalsensor data, biological extraction data, and the like, from immuneprotocol database 304. Computing device 104 may store and/or retrieveimmune profile machine-learning model 116, among other determinations,I/O data, models, and the like, in immune protocol database 304.

Continuing in reference to FIG. 3, immune protocol database 304 may beimplemented, without limitation, as a relational database, a key-valueretrieval database such as a NOSQL database, or any other format orstructure for use as a database that a person skilled in the art wouldrecognize as suitable upon review of the entirety of this disclosure.Immune protocol database 304 may alternatively or additionally beimplemented using a distributed data storage protocol and/or datastructure, such as a distributed hash table and the like. Immuneprotocol database 304 may include a plurality of data entries and/orrecords, as described above. Data entries in immune protocol database304 may be flagged with or linked to one or more additional elements ofinformation, which may be reflected in data entry cells and/or in linkedtables such as tables related by one or more indices in a relationaldatabase. Persons skilled in the art, upon reviewing the entirety ofthis disclosure, will be aware of various ways in which data entries ina database may store, retrieve, organize, and/or reflect data and/orrecords as used herein, as well as categories and/or populations of dataconsistent with this disclosure.

Further referring to FIG. 3, immune protocol database 304 may include,without limitation, immune biomarker table 308, immune profile table312, protocol element table 316, nutrient amount table 320, immuneprotocol table 324, and/or heuristic table 328. Determinations by amachine-learning process, machine-learning model, ranking function,and/or classifier, may also be stored and/or retrieved from the immuneprotocol database 304. As a non-limiting example, immune protocoldatabase 304 may organize data according to one or more instructiontables. One or more immune protocol database 304 tables may be linked toone another by, for instance in a non-limiting example, common columnvalues. For instance, a common column between two tables of immuneprotocol database 304 may include an identifier of a submission, such asa form entry, textual submission, accessory device tokens, local accessaddresses, metrics, and the like, for instance as defined herein; as aresult, a search by a computing device 104 may be able to retrieve allrows from any table pertaining to a given submission or set thereof.Other columns may include any other category usable for organization orsubdivision of data, including types of data, names and/or identifiersof individuals submitting the data, times of submission, and the like;persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various ways in which data from one or moretables may be linked and/or related to data in one or more other tables.

Continuing in reference to FIG. 3, in a non-limiting embodiment, one ormore tables of immune protocol database 304 may include, as anon-limiting example, immune biomarker table 308, which may includecategorized identifying data, as described above, including geneticdata, epigenetic data, microbiome data, physiological data, biologicalextraction data, and the like. Immune biomarker table 308 may includeimmune biomarker 108 categories according to gene expression patterns,SNPs, mutations, cytokine concentration, allergen data, data concerningmetabolism of protocol elements 144, pharmacokinetics, nutrientabsorption, and the like, categories, and may include linked tables tomathematical expressions that describe the impact of each immunebiomarker 108 datum on immune profile 112, for instance threshold valuesfor gene expression, and the like, as it relates to immunologicalfunction, immune category 120, and the like One or more tables mayinclude immune profile table 312, which may include data regardingimmune biomarker 108, thresholds, scores, metrics, values,categorizations, and the like, that system 100 may use to calculate,derive, filter, retrieve and/or store current immunological functionlevels, immunological dysfunctions, immune categories 120, and the like.One or more tables may include protocol element table 316, which mayinclude data on protocol elements 144 for instance classified to immunecategory 120, classified to data from alike subjects with similar immunebiomarker 108, immune profile 112, and the like, that system 100 may useto calculate, derive, filter, retrieve and/or store protocol elements144. One or more tables may include nutrient amount table 320, which mayinclude functions, model, equations, algorithms, thresholds, and thelike, used to calculate or derive nutrient amounts 152 relating toimmune profile 112 and/or immune category 120, may include nutrientamounts 152 organized by nutrient, nutrient classification, age, sex,symptom severity, and the like One of more tables may include immuneprotocol table 324, which may include protocol element 144 identifiers,elimination plan 128, reintroduction phase 140, frequency and magnitudesassociated with protocol elements 144, regarding times to eat,identifiers of meals, recipes, ingredients, diet types, and the like.One or more tables may include, without limitation, a heuristic table328, which may organize rankings, scores, models, outcomes, functions,numerical values, scales, arrays, matrices, and the like, that representdeterminations, probabilities, metrics, parameters, values, and thelike, include one or more inputs describing potential mathematicalrelationships, as described herein.

Referring now to FIGS. 4A and 4B, a non-limiting exemplary embodiment400 of an immune profile 112 is illustrated. Immune profile 112 mayinclude a variety of immune biomarker 108 categories, for instance 22distinct categories, as shown in FIGS. 4A and 4B. each immune biomarker108 may be assigned immune parameter, value, such as an arbitrary value,where some immune biomarkers 108, such as those shaded in light grey,may relate to absolute scales from [0, x], where x is a maximal valueand the range of values for the immune biomarker 108 cannot be below a‘zero amount’. Some immune biomarkers 108, such as those shaded in darkgrey, may relate to gene expression levels, wherein, the immunebiomarker 108 is enumerated as a ‘box plot’ that illustrates the rangeof expression in a population of users organized according to, forinstance tissue type. In such an example, the dashed line may relate toa ‘normal threshold’ above which is elevated gene expression, belowwhich is decreased expression level. Each immune biomarker 108 may haveassociated with it a numerical score, or some other identifyingmathematical value that computing device 104 may assign. Persons skilledin the art, upon benefit of this disclosure in full, may appreciate thatfor each user, any number of immune biomarkers 108 may be enumerated andassigned a value according to immune profile machine-learning model 116.Immune profile 112 may be graphed, or otherwise displayed, according tothe enumeration by immune profile machine-learning model 116. Forinstance, each bar of the bar graph, or combinations of bar graphcategories, may instruct a classification of a user's immune profile 112to immune category 120.

Still referring now to FIGS. 4A and 4B, in non-limiting exemplaryillustrations immune profile 112 may be classified to immune category120. Some and/or all of the immune biomarkers 108 summarized in immuneprofile 112 may be used to classify an individual to a particular immunecategory 120. For instance, as shown in FIG. 4B, ten of the 22 immunebiomarker 108 categories may be used to classify immune profile 112 toone or more immune categories 120. Alternatively or additionally, immuneprofile machine-learning model 116 may be trained to assign immunebiomarker 108 to immune category 120, wherein computing device 104 mayknow the identity of immune category 120 according to which immunecategory 120 has the most identifying data points.

Referring now to FIG. 5, a non-limiting exemplary embodiment 500 ofimmune protocol 156 is illustrated. Immune protocol 156 may include aschedule for arranging protocol elements 144, according to for instancea 24-hour timetable, as designated on the left, where consumption isplanned along a user's typical day-night cycle, beginning at ˜6 am untiljust after 6 pm. Protocol element 144 may include breakfast (denoted asmid-sized dark grey circle), which may correspond to a file ofbreakfast-related plurality of protocol elements 144 (denoted b1, b2,b3, b4 . . . bn, to the nth breakfast item). Protocol element 144 mayinclude snacks eaten throughout the day to, for instance achievenutrient amounts 152 missing from meals (denoted as small blackcircles), which may correspond to a file of snacking-related pluralityof protocol elements 144 (denoted s1, s2, s3, s4 . . . sn, to the nthsnacking item). Protocol element 144 may include dinner (denoted aslarge-sized light grey circle), which may correspond to a file ofdinner-related plurality of protocol elements 144 (denoted d1, d2, d3,d4 . . . dn, to the nth dinner item). Immune protocol 156 may include avariety of diets, as denoted in the monthly schedule at the bottom,Sunday through Saturday. Immune protocol 156 ‘C’ is shown, which may bean idealistic goal for user to achieve by the end of the month, wherenourishment plan ‘A’ and ‘B’ are intermediate plans intended to weanuser to the ‘ideal’ plan. Protocol elements 144 classified by ‘mealtype’ may be further modified by ‘A’ and ‘B’ according to userpreferences 148 collected by computing device 104 throughout theprocess. Circle sizes, denoting Protocol element 144 classes may relateto portion sizes, which are graphed along the circle corresponding tothe times they are expected to be consumed. User may indicate whichprotocol element 144 from each category was consumed, and when it wasconsumed, to arrive at immune score 164.

Referring now to FIG. 6, a non-limiting exemplary embodiment 600 of auser device 604 is illustrated. User device 604 may include computingdevice 104, a “smartphone,” cellular mobile phone, desktop computer,laptop, tablet computer, internet-of-things (IOT) device, wearabledevice, among other devices. User device 604 may include any device thatis capable for communicating with computing device 104, immune protocoldatabase 304, or able to receive, transmit, and/or display, via agraphical user interface, immune profile 112, Protocol element 144,immune protocol 156, immune score 164, among other outputs from system100. User device 604 may provide an immune profile 112, for instance asa collection of parameters determined from immune biomarker 108 data.User device 604 may provide immune category 120 that was determined as afunction of immune parameters enumerated in immune profile 112. Userdevice 604 may provide data concerning nutrient amounts 152, includingthe levels of specific nutrients, nutrient ranges, nutrients to avoid,and the like User device 604 may link timing of foods to preemptiveordering interface for ordering a protocol element 144, for instance andwithout limitation, through a designated mobile application, mappingtool or application, and the like, and a radial search method about auser's current location as described in U.S. Nonprovisional applicationSer. No. 17/087,745, filed Nov. 3, 2020, titled “A METHOD FOR AND SYSTEMFOR PREDICTING ALIMENTARY ELEMENT ORDERING BASED ON BIOLOGICALEXTRACTION,” the entirety of which is incorporated herein by reference.User device 604 may display protocol elements 144 as a function oflocation. User device 604 may link immune protocol 156 to a schedulingapplication, such as a ‘calendar’ feature on user device 604, which mayset audio-visual notifications, timers, alarms, and the like.

Continuing in reference to FIG. 6, user device 604 may include computingdevice 104, a “smartphone,” cellular mobile phone, desktop computer,laptop, tablet computer, internet-of-things (IOT) device, wearabledevice, among other devices. User device may include any device that iscapable for communicating with computing device 104, database, or ableto receive data, retrieve data, store data, and/or transmit data, forinstance via a data network technology such as 3G, 4G/LTE, 5G, Wi-Fi(IEEE 802.11 family standards), and the like. User device may includedevices that communicate using other mobile communication technologies,or any combination thereof, for short-range wireless communication (forinstance, using Bluetooth and/or Bluetooth LE standards, AirDrop, Wi-Fi,NFC, and the like), and the like.

Referring now to FIG. 7, an exemplary embodiment 700 of a method forgenerating an immune protocol for identifying and reversing autoimmunedisease is illustrated. At step 705, the method includes receiving, by acomputing device 104, at least an immune biomarker 108; this may beimplemented, without limitation, as described above in FIGS. 1-6.

Still referring to FIG. 7, at step 710, method includes retrieving, bythe computing device 104, an immune profile 112 related to the user.Retrieving the immune profile 112 related to the user may includetraining an immune profile machine-learning model 116 with the immuneprofile training data that includes a plurality of data entries whereineach entry correlates immune biomarkers 108 to a plurality of immuneparameters, and generating the immune profile 112 as a function of theimmune profile machine-learning model 116 and the at least an immunebiomarker 108; this may be implemented, without limitation, as describedabove in FIGS. 1-6.

Continuing in reference to FIG. 7, at step 715, method includesassigning, by the computing device 104, the immune profile 112 to animmune category 120, wherein the immune category 120 is a determinationabout a current immunological state of the user according to the atleast an immune biomarker 108. Assigning the immune profile 112 to animmune category 120 may include classifying the immune profile 112 to animmune category 120 using an immune classification machine-learningprocess 124 and assigning the immune category 120 as a function of theclassifying; this may be implemented, without limitation, as describedabove in FIGS. 1-6.

Continuing in reference to FIG. 1, at step 720, method includesdetermining, by the computing device 104, using the immune category 120and the immune profile 112, an elimination plan 128, wherein determiningthe elimination plan 128 includes identifying an effect on the immuneprofile 120 for each nutrition element of a plurality of nutritionelements, wherein the plurality of nutrition elements have been consumedby the user, determining, of the identified effect, at least a nutritionelement that may contribute to the immune category; this may beimplemented, without limitation, as described above in FIGS. 1-6.Identifying an effect on the immune profile 112 for each nutritionelement of a plurality of nutrition elements may include receivingnutritional input 132 from the user. Determining at least a nutritionelement that may contribute to the immune category 120 may includereceiving immune training data, training an immune machine-learningmodel 136 with immune training data that includes a plurality of dataentries wherein each entry correlates a plurality of nutrition amountsto immune biomarkers 108 and determining at least a nutrition elementthat may contribute to the immune category 120 as a function of theimmune machine-learning model 136 and the nutritional input 132 from theuser; this may be implemented, without limitation, as described above inFIGS. 1-6.

Continuing in reference to FIG. 7, at step 725, method includescreating, by the computing device 104, using the elimination plan 128, areintroduction phase 140, wherein creating the reintroduction phase 140includes identifying a frequency associated with the at least anutrition element determined in the elimination plan 128 and identifyinga magnitude associated with the at least a nutrition element determinedin the elimination plan 128. Identifying the frequency associated withthe at least a nutrition element determined in the elimination plan 128may include calculating a consumption time of the at least a nutritionelements as a function of the identified effect in the elimination plan128. Identifying the magnitude associated with the at least a nutritionelement may include calculating a serving size of the at least anutrition element as a function of the identified effect in theelimination plan 128; this may be implemented, without limitation, asdescribed above in FIGS. 1-6.

Continuing in reference to FIG. 7, at step 730, method includesidentifying, by the computing device 104, a plurality of protocolelements 144, wherein each protocol element 144 contains at least anutrient amount intended to prevent autoimmune disease. Identifying aplurality of protocol elements 144 may include training a nutritionmachine-learning process 148 with training data, wherein training dataincludes a plurality of data entries that correlates a plurality ofnutrient effects to a plurality of nutrient amounts 152 for each immunecategory 120, calculate the at least a nutrient amount as a function ofthe immune category 120 of the user, and identifying the plurality ofprotocol elements 144 according to the nutrition machine-learningprocess 148 and the at least a nutrient amount 152; this may beimplemented, without limitation, as described above in FIGS. 1-6.

Continuing in reference to FIG. 7, at step 735, method includesgenerating, by the computing device 104, an immune protocol 156 using,using the elimination plan 128, the reintroduction phase 140, and theplurality of protocol elements 144. Generating the immune protocol 156may include generating an objective function 160 with the plurality ofprotocol elements 144, wherein the objection function 160 outputs atleast an ordering of the plurality of protocol elements 144 according toconstraints in the reintroduction phase 140. Generating the immuneprotocol 156 may include generating an immune score 164; this may beimplemented, without limitation, as described above in FIGS. 1-6.

It is to be noted that any one or more of the aspects and embodimentsdescribed herein may be conveniently implemented using one or moremachines (e.g., one or more computing devices that are utilized as auser computing device for an electronic document, one or more serverdevices, such as a document server, and the like) programmed accordingto the teachings of the present specification, as will be apparent tothose of ordinary skill in the computer art. Appropriate software codingcan readily be prepared by skilled programmers based on the teachings ofthe present disclosure, as will be apparent to those of ordinary skillin the software art. Aspects and implementations discussed aboveemploying software and/or software modules may also include appropriatehardware for assisting in the implementation of the machine executableinstructions of the software and/or software module.

Such software may be a computer program product that employs amachine-readable storage medium. A machine-readable storage medium maybe any medium that is capable of storing and/or encoding a sequence ofinstructions for execution by a machine (e.g., a computing device) andthat causes the machine to perform any one of the methodologies and/orembodiments described herein. Examples of a machine-readable storagemedium include, but are not limited to, a magnetic disk, an optical disc(e.g., CD, CD-R, DVD, DVD-R, and the like), a magneto-optical disk, aread-only memory “ROM” device, a random access memory “RAM” device, amagnetic card, an optical card, a solid-state memory device, an EPROM,an EEPROM, and any combinations thereof. A machine-readable medium, asused herein, is intended to include a single medium as well as acollection of physically separate media, such as, for example, acollection of compact discs or one or more hard disk drives incombination with a computer memory. As used herein, a machine-readablestorage medium does not include transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as adata signal on a data carrier, such as a carrier wave. For example,machine-executable information may be included as a data-carrying signalembodied in a data carrier in which the signal encodes a sequence ofinstruction, or portion thereof, for execution by a machine (e.g., acomputing device) and any related information (e.g., data structures anddata) that causes the machine to perform any one of the methodologiesand/or embodiments described herein.

Examples of a computing device include, but are not limited to, anelectronic book reading device, a computer workstation, a terminalcomputer, a server computer, a handheld device (e.g., a tablet computer,a smartphone, and the like), a web appliance, a network router, anetwork switch, a network bridge, any machine capable of executing asequence of instructions that specify an action to be taken by thatmachine, and any combinations thereof. In one example, a computingdevice may include and/or be included in a kiosk.

Referring now to FIG. 8, a diagrammatic representation of one embodimentof a computing device in the exemplary form of a computer system 800within which a set of instructions for causing a control system toperform any one or more of the aspects and/or methodologies of thepresent disclosure may be executed is illustrated. It is alsocontemplated that multiple computing devices may be utilized toimplement a specially configured set of instructions for causing one ormore of the devices to perform any one or more of the aspects and/ormethodologies of the present disclosure. Computer system 800 includes aprocessor 804 and a memory 808 that communicate with each other, andwith other components, via a bus 812. Bus 812 may include any of severaltypes of bus structures including, but not limited to, a memory bus, amemory controller, a peripheral bus, a local bus, and any combinationsthereof, using any of a variety of bus architectures.

Continuing in reference to FIG. 8, processor 804 may include anysuitable processor, such as without limitation a processor incorporatinglogical circuitry for performing arithmetic and logical operations, suchas an arithmetic and logic unit (ALU), which may be regulated with astate machine and directed by operational inputs from memory and/orsensors; processor 804 may be organized according to Von Neumann and/orHarvard architecture as a non-limiting example. Processor 804 mayinclude, incorporate, and/or be incorporated in, without limitation, amicrocontroller, microprocessor, digital signal processor (DSP), FieldProgrammable Gate Array (FPGA), Complex Programmable Logic Device(CPLD), Graphical Processing Unit (GPU), general purpose GPU, TensorProcessing Unit (TPU), analog or mixed signal processor, TrustedPlatform Module (TPM), a floating point unit (FPU), and/or system on achip (SoC)

Continuing in reference to FIG. 8, memory 808 may include variouscomponents (e.g., machine-readable media) including, but not limited to,a random-access memory component, a read only component, and anycombinations thereof. In one example, a basic input/output system 816(BIOS), including basic routines that help to transfer informationbetween elements within computer system 800, such as during start-up,may be stored in memory 808. Memory 808 may also include (e.g., storedon one or more machine-readable media) instructions (e.g., software) 820embodying any one or more of the aspects and/or methodologies of thepresent disclosure. In another example, memory 808 may further includeany number of program modules including, but not limited to, anoperating system, one or more application programs, other programmodules, program data, and any combinations thereof.

Continuing in reference to FIG. 8, computer system 800 may also includea storage device 824. Examples of a storage device (e.g., storage device824) include, but are not limited to, a hard disk drive, a magnetic diskdrive, an optical disc drive in combination with an optical medium, asolid-state memory device, and any combinations thereof. Storage device824 may be connected to bus 812 by an appropriate interface (not shown).Example interfaces include, but are not limited to, SCSI, advancedtechnology attachment (ATA), serial ATA, universal serial bus (USB),IEEE 1394 (FIREWIRE), and any combinations thereof. In one example,storage device 824 (or one or more components thereof) may be removablyinterfaced with computer system 800 (e.g., via an external portconnector (not shown)). Particularly, storage device 824 and anassociated machine-readable medium 828 may provide nonvolatile and/orvolatile storage of machine-readable instructions, data structures,program modules, and/or other data for computer system 800. In oneexample, software 820 may reside, completely or partially, withinmachine-readable medium 828. In another example, software 820 mayreside, completely or partially, within processor 804.

Continuing in reference to FIG. 8, computer system 800 may also includean input device 832. In one example, a user of computer system 800 mayenter commands and/or other information into computer system 800 viainput device 832. Examples of an input device 832 include, but are notlimited to, an alpha-numeric input device (e.g., a keyboard), a pointingdevice, a joystick, a gamepad, an audio input device (e.g., amicrophone, a voice response system, and the like), a cursor controldevice (e.g., a mouse), a touchpad, an optical scanner, a video capturedevice (e.g., a still camera, a video camera), a touchscreen, and anycombinations thereof. Input device 832 may be interfaced to bus 812 viaany of a variety of interfaces (not shown) including, but not limitedto, a serial interface, a parallel interface, a game port, a USBinterface, a FIREWIRE interface, a direct interface to bus 812, and anycombinations thereof. Input device 832 may include a touch screeninterface that may be a part of or separate from display 836, discussedfurther below. Input device 832 may be utilized as a user selectiondevice for selecting one or more graphical representations in agraphical interface as described above.

Continuing in reference to FIG. 8, a user may also input commands and/orother information to computer system 800 via storage device 824 (e.g., aremovable disk drive, a flash drive, and the like) and/or networkinterface device 840. A network interface device, such as networkinterface device 840, may be utilized for connecting computer system 800to one or more of a variety of networks, such as network 844, and one ormore remote devices 848 connected thereto. Examples of a networkinterface device include, but are not limited to, a network interfacecard (e.g., a mobile network interface card, a LAN card), a modem, andany combination thereof. Examples of a network include, but are notlimited to, a wide area network (e.g., the Internet, an enterprisenetwork), a local area network (e.g., a network associated with anoffice, a building, a campus or other relatively small geographicspace), a telephone network, a data network associated with atelephone/voice provider (e.g., a mobile communications provider dataand/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network, such as network 844,may employ a wired and/or a wireless mode of communication. In general,any network topology may be used. Information (e.g., data, software 820,and the like) may be communicated to and/or from computer system 800 vianetwork interface device 840.

Continuing in reference to FIG. 8, computer system 800 may furtherinclude a video display adapter 852 for communicating a displayableimage to a display device, such as display device 836. Examples of adisplay device include, but are not limited to, a liquid crystal display(LCD), a cathode ray tube (CRT), a plasma display, a light emittingdiode (LED) display, and any combinations thereof. Display adapter 852and display device 836 may be utilized in combination with processor 804to provide graphical representations of aspects of the presentdisclosure. In addition to a display device, computer system 800 mayinclude one or more other peripheral output devices including, but notlimited to, an audio speaker, a printer, and any combinations thereof.Such peripheral output devices may be connected to bus 812 via aperipheral interface 856. Examples of a peripheral interface include,but are not limited to, a serial port, a USB connection, a FIREWIREconnection, a parallel connection, and any combinations thereof.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments, what has been described herein is merelyillustrative of the application of the principles of the presentinvention. Additionally, although particular methods herein may beillustrated and/or described as being performed in a specific order, theordering is highly variable within ordinary skill to achieve methods,systems, and software according to the present disclosure. Accordingly,this description is meant to be taken only by way of example, and not tootherwise limit the scope of this invention.

Exemplary embodiments have been disclosed above and illustrated in theaccompanying drawings. It will be understood by those skilled in the artthat various changes, omissions and additions may be made to that whichis specifically disclosed herein without departing from the spirit andscope of the present invention.

What is claimed is:
 1. A system for generating an immune protocol foridentifying and reversing immune disease, the system comprising: acomputing device, wherein the computing device is configured to: receiveat least an immune biomarker from a graphical user interface; assign animmune category as a function of the immune biomarker; identify aneffect on an immune profile for each nutritional element of a pluralityof nutritional elements, wherein the plurality of nutrition elementshave been consumed by the user and the identification includes:receiving a nutritional input from the graphical user interface;retrieving a plurality of predicted effects of the plurality of nutrientamounts on the immune profile as a function of the nutritional input;and calculating an effect of the nutritional input on the immuneprofile; determine at least a nutritional element that contributes tothe immune category as a function of an immune machine-learning modeland the nutritional input; identify a plurality of protocol elements,wherein each protocol element contains at least a nutrient amountintended to address the immunological dysfunction; and generate animmune protocol as a function of the plurality of protocol elements. 2.The system of claim 1, wherein the immune biomarker includes at least atest result relating to a current immunological state of the user. 3.The system of claim 1, wherein the immune profile includes adetermination of a current immunological state of the user.
 4. Thesystem of claim 1, wherein the immune category includes tissue typeclassification.
 5. The system of claim 1, wherein assigning an immunecategory includes assigning the immune category as a function of animmune classification machine learning process.
 6. The system of claim1, wherein the computing device is further configured to determine anelimination plan as a function of the at least a nutritional element. 7.The system of claim 6, wherein determining the elimination plan includesdetermining if a change in the immune category arises from adding anutrient to the nutritional input.
 8. The system of claim 6, whereindetermining the elimination plan includes calculating an immune index.9. The system of claim 6, wherein the computing device is furtherconfigured to: identify a frequency associated with the at least anutrition element; and create a reintroduction phase as a function ofthe elimination plan.
 10. The system of claim 1, wherein the immuneprotocol comprises the nutrient amounts and the plurality of protocolelements organized according to constraints in an elimination plan and areintroduction phase.
 11. A method for generating an immune protocol foridentifying and reversing immune disease, the method comprising:receiving, at a computing device, at least an immune biomarker from agraphical user interface; assigning, at a computing device, an immunecategory as a function of the immune biomarker; identifying, at acomputing device, an effect on an immune profile for each nutritionalelement of a plurality of nutritional elements, wherein the plurality ofnutrition elements have been consumed by the user and the identificationincludes: receiving a nutritional input from the graphical userinterface; retrieving a plurality of predicted effects of the pluralityof nutrient amounts on the immune profile as a function of thenutritional input; and calculating an effect of the nutritional input onthe immune profile; determining, at a computing device, at least anutritional element that contributes to the immune category as afunction of an immune machine-learning model and the nutritional input;identifying, at a computing device, a plurality of protocol elements,wherein each protocol element contains at least a nutrient amountintended to address the immunological dysfunction; and generating, at acomputing device, an immune protocol as a function of the plurality ofprotocol elements.
 12. The method of claim 11, wherein the immunebiomarker includes at least a test result relating to a currentimmunological state of the user.
 13. The method of claim 11, wherein theimmune profile includes a determination of a current immunological stateof the user.
 14. The method of claim 11, wherein the immune categoryincludes tissue type classification.
 15. The method of claim 11, whereinassigning an immune category includes assigning the immune category as afunction of an immune classification machine learning process.
 16. Themethod of claim 11, wherein the computing device is further configuredto determine an elimination plan as a function of the at least anutritional element.
 17. The method of claim 16, wherein determining theelimination plan includes determining if a change in the immune categoryarises from adding a nutrient to the nutritional input.
 18. The methodof claim 16, wherein determining the elimination plan includescalculating an immune index.
 19. The method of claim 16, wherein thecomputing device is further configured to: identify a frequencyassociated with the at least a nutrition element; and create areintroduction phase as a function of the elimination plan.
 20. Themethod of claim 11, wherein the immune protocol comprises the nutrientamounts and the plurality of protocol elements organized according toconstraints in an elimination plan and a reintroduction phase.